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Background: Ambient air pollution during pregnancy has been linked with postpartum depression up to 12 months, but few studies have investigated its impact on persistent depression beyond 12 months postpartum. This study aimed to evaluate prenatal ambient air pollution exposure and the risk of persistent depression over 3 years after childbirth and to identify windows of susceptibility., Methods: This study included 361 predominantly low-income Hispanic/Latina participants with full-term pregnancies in the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) cohort. We estimated daily residential PM 2.5 , PM 10 , NO 2 , and O 3 concentrations throughout 37 gestational weeks using inverse-distance squared spatial interpolation from monitoring data and calculated weekly averaged levels. Depression was assessed by the 20-item Center for Epidemiologic Studies-Depression (CES-D) scale at 12, 24, and 36 months postpartum, with persistent postpartum depression defined as a CES-D score ≥16 at any of these timepoints. We performed robust Poisson log-linear distributed lag models (DLM) via generalized estimating equations (GEE) to estimate the adjusted risk ratio (RR)., Results: Depression was observed in 17.8 %, 17.5 %, and 13.4 % of participants at 12, 24, and 36 months, respectively. We found one IQR increase (3.9 ppb) in prenatal exposure to NO 2 during the identified sensitive window of gestational weeks 13-29 was associated with a cumulative risk ratio of 3.86 (95 % CI: 3.24, 4.59) for persistent depression 1-3 years postpartum. We also found one IQR increase (7.4 μg/m 3 ) in prenatal exposure to PM 10 during gestation weeks 12-28 was associated a cumulative risk ratio of 3.88 (95 % CI: 3.04, 4.96) for persistent depression. No clear sensitive windows were identified for PM 2.5 or O 3 ., Conclusions: Mid-pregnancy PM 10 and NO 2 exposures were associated with nearly 4-fold increased risks of persistent depression after pregnancy, which has critical implications for prevention of perinatal mental health outcomes., Competing Interests: Declaration of competing interest We declare no actual or potential competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Despite increasing evidence of a strong correlation between air pollution and otitis media (OM), the impact of early-life ozone (O 3 ) exposure on the development of OM in children remains uncertain., Objectives: To explore the connection between early-life O 3 exposure and OM, and to identify the critical time period(s) during which O 3 exposure significantly influences the development of OM in children., Methods: We conducted a study involving 8689 children living in Changsha, China. Information regarding personal factors, health conditions, and the indoor environment was gathered using questionnaires. Personal exposure to outdoor O 3 and other major pollutants at the place of residence during the periods before conception, prenatal periods, and after birth was calculated by applying the inverse distance weighted (IDW) method with data gathered from ten air quality monitoring stations. Multiple logistic regression analyses were employed to investigate the associations between O 3 exposure and children's OM., Results: After controlling for covariates and ambient temperature, exposure to O 3 during the year preceding pregnancy was correlated with childhood lifetime OM, showing ORs (95 % CI) of 1.28 (1.01-1.64). O 3 exposures in the 10th-12th, 7th-9th, and 4th-6th months before pregnancy were all linked to children's lifetime OM. Within the multi-window model, we detected that O 3 exposure in the 10th to 12th month prior to pregnancy was significantly related to lifetime OM, showing ORs (95 % CI) of 1.28 (1.05-1.55). A significant link was discovered between childhood OM and O 3 exposure after controlling for six other pollutants (SO 2 , PM 2.5 , NO 2 , PM 2.5 - 10 , CO, and PM 10 ) during the 10th to 12th month prior to conception. Exposure to O 3 during the 36th gestational week significantly raised the likelihood of childhood lifetime OM. There is a significant interaction between O 3 and temperature exposure during the first trimester of pregnancy and one year before pregnancy on childhood lifetime OM., Conclusions: Preconceptional O 3 exposure and its interaction with low temperature played critical roles in children's OM development, backing the hypothesis of "(pre) fetal origins of childhood OM"., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: While ambient air pollution has been associated with fetal growth in singletons, its correlation among twins is not well-established due to limited research in this area., Methods: The effects of exposure to PM 2.5 particulate matter and its main components during pregnancy on birth weight and the incidence of large for gestational age (LGA) were investigated in 6177 twins born after in vitro fertilization at the Center for Reproductive Medicine of Shanghai Ninth People's Hospital (Shanghai, China) between 2007 and 2021. Other birth weight-related outcomes included macrosomia, low birth weight, very low birth weight, and small for gestational age (SGA). The associations of PM 2.5 exposure with birth weight outcomes were analyzed using linear mixed-effect models and random-effect logistic regression models. Distributed lag models were incorporated to estimate the time-varying associations., Results: The findings revealed that an interquartile range (IQR) increase (18 μg/m 3 ) in PM 2.5 exposure over the entire pregnancy was associated with a significant increase (57.06 g, 95 % confidence interval [CI]: 30.91, 83.22) in the total birth weight of twins. The effect was more pronounced in larger fetuses (34.93 g, 95 % CI: 21.13, 48.72) compared to smaller fetuses (21.77 g, 95 % CI: 6.94, 36.60) within twin pregnancies. Additionally, an IQR increase in PM 2.5 exposure over the entire pregnancy was associated with a 34 % increase in the risk of LGA (95 % CI: 11 %, 63 %). Furthermore, specific chemical components of PM 2.5 , such as sulfate (SO 4 2- ), exhibited effect estimates comparable to the PM 2.5 total mass., Conclusion: Overall, the findings indicate that exposures to PM 2.5 and its specific components are associated with fetal overgrowth in twins., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: The current evidence regarding the association between long-term exposure to ozone (O 3 ) and hypertension incidence is limited and inconclusive, particularly at low O 3 concentrations. Therefore, our research aims to investigate the potential link between long-term O 3 exposure and hypertension in a region with low pollution levels., Methods: From 2010 to 2012, we conducted a cohort prospective study by recruiting nearly 10,000 attendees through multistage cluster random sampling in Guizhou Province, China. These individuals were followed up from 2016 to 2020, and 5563 cases were finally included in the analysis. We employed a high-resolution model with both temporal and spatial accuracy to estimate the maximum daily 8-h average O 3 and utilized annual average O 3 concentrations for three exposure periods (2009_10, 2007_10, 2005_10) as the exposure indicator. Time-dependent covariates Cox regression model was exerted to estimate the hazard ratios (HRs) of hypertension incidence. Generalized linear model was employed to assess the association between O 3 and systolic, diastolic, pulse, and mean arterial pressure. The dose-response curve was explored using a restricted cubic spline function., Results: 1213 hypertension incidents occurred during 39,001.80 person-years, with an incidence density of 31.10/1000 Person Years (PYs). The average O 3 concentrations during the three exposure periods were 66.76 μg/m 3 , 67.85 μg/m 3 , and 67.21 μg/m 3 , respectively. Per 1 μg/m 3 increase in O 3 exposure was associated with 11 % increase in the incidence of hypertension in the single-pollution model, and the association was more pronounced in Han, urban, and higher altitude areas. SBP, PP, and MAP were increased by 0.619 (95 % CI, 0.361-0.877) mm Hg, 0.477 (95 % CI, 0.275-0.679) mm Hg, 0.301 (95 % CI, 0.127-0.475) mm Hg, respectively. Furthermore, we observed a nonlinear exposure-response relationship between O 3 and hypertension incidence., Conclusions: Long-term exposure to low-level O 3 exposure is associated with an increased risk of hypertension., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Nitrate (NO 3 - ) is an important contributor to PM 2.5 which can adversely affect the environment and human health. A noticeable decrease in NO x concentrations has been reported due to the lockdown measures implemented to curb the spread of Corona Virus Disease 2019 (COVID-19). However, questions remain, regarding the nonlinear relationship between NO x and NO 3 - . Here, we collected PM 2.5 samples in two periods, before and during the lockdown of COVID-19 in Shanghai. Dual isotopes (δ 18 O-NO 3 - and δ 15 N-NO 3 - ) of NO 3 - were measured to investigate the formation pathways and potential sources of NO 3 - . The results showed that the concentration of NO 3 - decreased significantly during the lockdown period compared to the period before the lockdown. Additionally, the hydroxyl pathway was the dominant contributor to NO 3 - production during the lockdown period, while N 2 O 5 hydrolyses dominated the formation of NO 3 - before the lockdown. This change is largely attributable to alterations in the oxidative potential of the environment. In comparison to the period preceding the lockdown, the relative contributions of each NO x source remained largely unchanged throughout the lockdown periods. Nevertheless, the concentration of NO 3 - contributed by each NO x source exhibited a notable decline, particularly the mobile sources and coal combustion. Furthermore, the reduction extent of NO 3 - due to the lockdown period was also greater than the reduction during the Clean Air Actions (2013-2017). Our findings provide evidence that the COVID-19 lockdown led to a decrease in NO 3 - concentration due to changes in the formation pathway and reductions in NO x emissions from various sources., Competing Interests: Declaration of competing interest The authors declare that they have no conflicts of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The air quality index (AQI), based on criteria for air contaminants, is defined to provide a shared vision of air quality. As air pollution continues to rise in global cities due to urbanization and climate change, air pollution monitoring and forecasting models for effective air quality monitoring that gather and forecast information about air pollution concentration are essential in every city. Air quality predictions have evolved to be more helpful for management. Recently, better performance and ability have developed due to the involvement of machine learning (ML) and artificial intelligence (AI) in forecasting air quality in urban cities in India. This paper focuses on air pollution as a significant ecological problem that directly impacts human health and the distribution of an environmental system in urban areas. Hence, we have developed advanced models for daily AQI forecasting to understand the air effluence level in the upcoming days. In this research, six data-driven models have been developed and implemented for daily AQI forecasting in the study area; it is crucial for understanding the future air pollution levels to plan and control air pollution in the entire city. The developed model is applied to air quality datasets. A comparison of the performance of ML models tested here indicates that the XGBoost algorithm achieves the highest coefficient of determination (R 2 ) and root-mean-square deviation (RMSE) value of 0.99 and lower values value of 4.65 than other models in the testing phase. The results of the artificial neural network (ANN) algorithm are slightly lower than the extreme gradient boosting (XGBoost model); the ANN model results are as R 2 , mean squared error (MSE), and RMSE values of 0.99, 13.99, and 198.88, respectively. All the models were subjected to a ten-fold cross-validation model. However, the RF cross-validation model outperforms other models; the RF model result shows the R 2 , RMSE, and MSE values of 0.99, 3.64, and 4.12, respectively. This study also employed two interpretable models, namely feature importance analysis and Shapley additive explanation (SHAP), to evaluate both the global and local methods in a manner that is independent of specific ML models. The feature importance shows that particle matter (PM) 2.5, PM10, carbon monoxide (CO), and nitrogen oxides (NO x ) were the most influential variables. The results determined that such novel DL and ML models may improve the accuracy of AQI forecasts and understanding of air pollution, particularly in metropolitan cities., Competing Interests: Declarations Consent for publication All authors approved the final manuscript. Disclaimer All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Competing interests The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Air pollution from transport hubs is a recognised health concern for local urban inhabitants. Within the domain of transport hubs, significant attention has been given to larger airport and port settings, however concerns have been raised about emissions from urban railway hubs, especially those with diesel trains. This paper presents an approach that adopts low-cost monitoring (LCM) for fixed site monitoring (FSM) to quantify and disaggregate PM 2.5 and NO 2 contributions from railway station and road traffic on air quality in the vicinity of railway station in Dublin, Ireland. The NO 2 sensor showed larger discrepancies than the PM 2.5 sensor when compared to the reference monitor. Machine learning models (XGBoost and Random Forest (RF) regression) were applied to calibrate the LCM devices, with the XGBoost model (NO 2, R 2  = 0.8 and RSME = 9.1 μg/m 3 & PM 2.5 , R 2  = 0.92 and RSME = 2.2 μg/m 3 ) deemed more appropriate than the RF model. Local wind conditions, pressure, PM 2.5 concentrations, and road traffic significantly impacted NO 2 model results, while raw PM 2.5 sensor readings greatly influenced the PM 2.5 model output. This highlights that the NO 2 sensor requires more input data for accurate calibration, unlike the PM 2.5 sensor. The monitoring results from the one-month monitoring campaign from May 25, 2023 to June 25, 2023 presented elevated NO 2 and PM 2.5 concentrations measured at the railway station, which translated to exceedances of the annual WHO limits (PM 2.5  = 5 μg/m 3 , NO 2  = 10 μg/m 3 ) by 1.6-1.8 and 3.2-5.2 times respectively at the study site. A subsequent data filtering technique based on wind orientation, revealed that the railway station was the main PM 2.5 source and road traffic was the main NO 2 source when winds come from the railway station. This study highlights the value of LCM devices alongside robust machine learning techniques to capture air quality in urban settings., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Shanmuga Priyan reports financial support was provided by Environmental Protection Agency. Yuxuan Guo reports financial support was provided by Environmental Protection Agency. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Thermal power plants serve as significant CO 2 sources, and accurate monitoring of their emissions is crucial for improving the precision of global carbon emission estimates. In this study, a measurement method based on measuring point source plumes was employed in ground-based remote sensing experiments at the thermal power plant. By simulating CO 2 plumes, we analyzed the impact of surrounding urban structures, the geometric relationship between measurement points and plumes, and the influence on measurement points selection. We also assessed the capability and uncertainties in quantifying CO 2 emissions. For the Hefei power plant, CO 2 emission estimates were on average 7.98 ± 10.01 kg/s higher with surface buildings compared to scenarios without buildings (approximately 4.09% error). By selectively filtering discrete data, the emission estimation errors were significantly reduced by 7.31 ± 7.13 kg/s compared to pre-filtered data. Regarding the relationship between observation paths and plume geometry, simulation studies indicated that the ability to estimate CO 2 emissions varied for near and middle segment observations. The lowest emission rate error was found in the mid-segment near 1.5-2.0 km, reaching 7.13 ± 5.39 kg/s. CO 2 distribution at the mid-segment position becomes more uniform relative to the near segment, making it more suitable for meeting emission estimation requirements. Optimizing measurement schemes by considering environmental factors and precisely selecting measurement points significantly enhances emission estimation accuracy, providing crucial technical support for top-down estimates of anthropogenic CO 2 emissions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Setting: off fireworks during the Spring Festival (SF) is a traditional practice in China. However, because of its environmental impact, the Chinese government has banned this practice completely. Existing evaluations of the effectiveness of firework prohibition policies (FPPs) lack spatiotemporal perspectives, making it difficult to comprehensively assess their effects on air quality. Consequently, this study used remote sensing technology based on aerosol optical depth and multiple variables, compared nine statistical learning methods, and selected the optimal model, transformer, to estimate daily spatiotemporal continuous PM 2.5 concentration datasets for Tianjin from 2016 to 2020. The overall model accuracy reached a root mean square error of 15.30 μg/m³, a mean absolute error of 9.55 μg/m³, a mean absolute percentage error of 21.07%, and an R 2 of 0.88. Subsequently, we analysed the variations in PM 2.5 concentrations from three time dimensions-the entire year, winter, and SF periods-to exclude the impact of interannual variations on the experimental results. Additionally, we quantitatively estimated firework-specific PM 2.5 concentrations based on time-series forecasting. The results showed that during the three years following the implementation of the FPPs, firework-specific PM 2.5 concentrations decreased by 52.70%, 49.76%, and 86.90%, respectively, compared to the year before the implementation of the FPPs. Spatially, the central urban area and industrial zones are more affected by FPPs than the suburbs. However, daily variations of PM 2.5 concentrations during the SF showed that high concentrations of PM 2.5 produced in a short period will return to normal rapidly and will not cause lasting effects. Therefore, the management of fireworks needs to consider both environmental protection and the public's emotional attachment to traditional customs, rather than simply imposing a blanket ban on fireworks. We advocate improving firework policies in four aspects-production, sales, supervision, and control-to promote sustainable development of the ecological environment and human society., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Assessing the emissions of heavy-duty diesel trucks (HDDTs) is crucial for managing air quality in megacities, especially concerning nitrogen oxides (NO X ) and black carbon (BC). This study employed mobile plume chasing to monitor the real-world emissions of over 7778 HDDTs in Shenzhen. The findings indicate that the real-world NO X emission factors (EF) of China IV trucks did not differ significantly from those of China III, whereas China V and VI vehicles demonstrated fleet-averaged reductions of 27% and 85%, respectively. For China V, a significant decrease in the NO X EF for HDDTs registered after 2017 was attributed to the installation of advanced aftertreatment systems, including diesel oxidation catalysts (DOC) and Diesel Particle Filters (DPF), along with selective catalytic reduction (SCR). These technologies led to an average reduction of 42% in NO X and 61% in BC emissions. Seasonal variations were pronounced, with winter (∼20 °C) NO X EF 40% higher than summer (∼35 °C) levels. Conversely, BC EF decreased by 26% in winter, indicating significant impacts of ambient temperature on emissions. Spatial analysis revealed that the average NO X EF of HDDTs on east freeways was 1.4 times higher than that on urban expressways, influenced by variations in the proportion of vehicle types segmented by usage. These findings offer a comprehensive perspective on HDDTs emissions, highlighting the importance of large-scale emission monitoring through plume chasing for precise and effective control of real-world HDDTs emissions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Air quality degradation presents a significant public health challenge, particularly in rapidly urbanizing regions where changes in land use/land cover (LULC) can dramatically influence pollution levels. This study investigates the association between LULC changes and air pollution (AP) in the five fastest-growing cities of Bangladesh from 1998 to 2021. Leveraging satellite data from Landsat and Sentinel-5P, the analysis reveals a substantial increase in urban areas and sparse vegetation, with declines in dense vegetation and water bodies over this period. Urban expansion was most pronounced in Sylhet (22-254%), while Khulna experienced the largest increase in sparse vegetation (2-124%). Dense vegetation loss was highest in Dhaka (20-77%) and water bodies (9-59%) over this period. Concentrations of six major air pollutants (APTs) - aerosol index, CO, HCHO, NO 2 , O 3 , and SO 2 - were quantified, showing alarmingly high levels in densely populated industrial and commercial zones. Pearson's correlation indicates strong positive associations between APTs and urban land indices (R > 0.8), while negative correlations exist with vegetation indices. Geographically weighted regression modeling identifies city centers with dense urban built-up as pollution hotspots, where APTs exhibited stronger impacts on land cover changes (R 2  > 0.8) compared to other land classes. The highest daily emissions were observed for O 3 (1031 tons) and CO (356 tons) at Chittagong in 2021. In contrast, areas with substantial green cover displayed weaker pollutant-land cover associations. These findings underscore how unplanned urbanization drives AP by replacing natural land cover with emission sources, providing crucial insights to guide sustainable urban planning strategies integrating pollution mitigation and environmental resilience., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Cities, contributing over 70% of global emissions, are key areas for climate change mitigation. Heterogeneity within cities determines the need for spatialized urban emissions reduction policies. However, few studies have attempted to characterize the spatial distribution of carbon emissions at the urban scale. To address this issue, a novel mapping method was proposed, using Xi'an as an example to explore the spatial distribution of carbon emissions at the city scale. Firstly, multiple geospatial open-source data, such as point of interest (POI), road networks, and land use, were utilized to identify the locations of emission sources. High-resolution carbon emission distributions were then mapped by allocating emissions based on the Intergovernmental Panel on Climate Change (IPCC) methodology. The study employed Global Moran's I to analyze the changes in spatial heterogeneity at different resolutions. Additionally, the Local Indicators of Spatial Association index (LISA) and Standard Deviation Ellipses (SDE) were adopted to examine the spatiotemporal characteristics of carbon emissions in Xi'an. The results show that carbon emissions at Xi'an City rises from 45.112 million tons to 72.701 million tons between 2010 and 2021. The construction of multi-scale carbon emissions spatial distributions, with a resolution of up to 30 m, allowed for a more detailed characterization of carbon emissions, especially in urban fringe areas. In addition, the results indicate that urban carbon emissions exhibit the strongest spatial autocorrelation at a resolution of 350 m. The study can provide a reference for the development of regional carbon emission reduction policies and spatial planning. In addition, the proposed spatialized method of city carbon emissions depends on open-source data, which allows it to have the potential for application in other cities., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ling Han reports financial support was provided by Shaanxi University of Technology. Ziyan Liu reports financial support was provided by Chang'an University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Asthma is a prevalent chronic respiratory disease among children, influenced by various climate and environmental factors. Despite its prevalence, the specific effects of these factors on asthma remain unclear. This study aims to systematically assess the epidemiological evidence using spatial and temporal methods on the impact of climate and environmental factors on childhood asthma., Methods: A systematic review was conducted to analyse the impact of climate and environmental factors on childhood asthma and wheezing, focusing on spatial and temporal trends. Searches were carried out in PubMed, Embase, and CINAHL databases for studies published from January 2000 to April 2024, using key search terms 'asthma/wheezing', 'extreme weather, 'green space', 'air pollution' and 'spatial or temporal analyses"., Results: The systematic review analysed 28 studies, with six employing spatial and 22 using temporal analysis methods; however, none incorporated spatio-temporal analysis in their models. The findings reveal that extreme weather events, including heatwaves and heavy rainfall, elevate childhood asthma risks across various climates, with significant effects observed during summer and winter months. Dust storms in arid and subtropical regions are linked to immediate spikes in hospital admissions due to asthma exacerbations. The effects of green spaces on childhood asthma are mixed, with some studies indicating protective effects while others suggest increased risks, influenced by local environmental factors. Air pollutants such as PM 2.5 , NO 2 , and ozone can exacerbate asthma symptoms and along with other environmental factors, contribute to seasonal effects. High temperatures generally correlate with increased asthma risks, though the effects vary by age, sex, and climate., Conclusion: Future research should integrate spatial and temporal methods to better understand the effects of environmental and climate changes on childhood asthma., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The impact of atmospheric pollution on the growth of European forest tree species, particularly European beech, Silver fir and Norway spruce, is examined in five mesic forests in the Czech Republic. Analyzing of basal area increment (BAI) patterns using linear mixed effect models reveals a complex interplay between atmospheric nitrogen (N) and sulphur (S) deposition, climatic variables and changing CO 2 concentrations. Beech BAI responds positively to N deposition (in tandem with air CO 2 concentration), with soil phosphorus (P) availability emerging as a significant factor influencing overall growth rates. Fir BAI, on the other hand, was particularly negatively influenced by S deposition, although recent growth acceleration suggests growth resilience in post-pollution period. This fir growth surge likely coincides with stimulation of P acquisition following the decline of acidic pollution. The consequence is the current highest productivity among the studied tree species. The growth dynamics of both conifers were closely linked to the stoichiometric imbalance of phosphorus in needles, indicating the possible sensitivity of exogenous controls on nutrient uptake. Furthermore, spruce BAI was positively linked to calcium availability across sites. Despite enhanced water-use efficiency under elevated CO 2 , spruce growth is constrained by precipitation deficit and demonstrates weakening resilience to increasing growing season air temperatures. Overall, these findings underscore the intricate relationships between atmospheric pollution, nutrient availability, and climatic factors in shaping the growth dynamics of European forest ecosystems. Thus, incorporating biogeochemical context of nutrient availability is essential for realistic modelling of tree growth in a changing climate., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The prevalence of trace elements from industrial and traffic activities poses potential health risks through inhalation exposure. Prior studies have focused on trace elements in water, food, and dust, and less attention has been paid to their occurrence in fine particulate matter (PM 2.5 ). In this study, 1424 air samples were collected from three districts (Nanshan, Longgang, and Yantian) in Shenzhen from 2016 to 2021, and we analyzed the concentrations, temporal trends, and spatial distributions of PM 2.5 and associated trace elements. Both PM 2.5 and trace elements exhibited decreasing trends and similar seasonal variations, with high levels in cold seasons and low levels in warm seasons. In terms of spatial distributions, the concentrations of PM 2.5 and trace elements in Nanshan and Longgang were significantly higher than those in Yantian, likely due to the industrial structure and traffic activities. It is worth noting that PM 2.5 was identified as a potential mediator of the effect of meteorological parameters on trace element levels. Besides, the values of estimated daily intake (EDI) and uptake (EDU) suggested that infants and young children experienced an elevated risk of exposure to trace elements. While the annual average excess hazard indexes (R) were below the safety threshold (10 -6 ), carcinogenic trace elements like arsenic (As) and chromium (Cr) posed a greater potential threat to human health compared to non-carcinogenic trace elements., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Traffic emissions are an important source of air pollution worldwide, but in the Middle East, this problem is exacerbated by weak or no enforcement of emission regulations. Comprehensive measurements of fine PM emission factors (EFs) from road transport in the region have not yet been conducted, but such data are necessary for quantitative assessments of the health impact of transport emissions in the region. To address this need, PM 2.5 samples collected inside the Salim Slam tunnel in Beirut, Lebanon were analyzed for carbonaceous matter (organic carbon (OC) and elemental carbon (EC)), water-soluble ions, elements, and selected organic compounds. The OC/EC ratio was 1.8 for the total fleet and 2.6 for light-duty vehicles (LDV), in agreement with the dominant proportion of gasoline LDV in the Lebanese fleet. A Cu/Sb ratio of 4.2 ± 0.1 was observed, offering a valuable metric for detecting brake wear emissions in subsequent studies conducted in the region. The EFs of carbonaceous matter, elements and ions generally varied by a factor 0.1 and 10 in comparison to literature values, while those for alkanes and polycyclic aromatic hydrocarbons were similar to the upper values previously reported. The average number size distribution was characterized by a single mode around 35 nm. The particles number EF (for diameters between 10 and 480 nm) was within the range of 10 14 -10 15 particles per kg of fuel. The chemical mass balance model showed an average contribution to EF of 62% from non-exhaust sources. This study highlights the need for more enforceable stringent vehicular regulations because of the local practices (i.e., removal of catalyst) and some EF values are very high compared to other studies/countries., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Air pollutants have been investigated to be associated with many health issues. Recently, increasing epidemiological studies have suggested the association between air pollution exposure and risk of frailty with inconsistent findings. This systematic review and meta-analysis was to summarize and evaluate effects of exposure to various air pollutants on risk of frailty. PubMed, Embase, Scopus and Web of Science were systematically searched for relevant studies published before May 11, 2024. Studies that explored the potential relationship between exposure to air pollutants (PM 2.5 , PM 10 , O 3 , NO x , solid fuel, secondhand tobacco, and air quality) and risk of frailty were included. The quality of cross-sectional and cohort studies was evaluated using an eight-item assessment instrument for epidemiological studies and Newcastle-Ottawa Scale, respectively. A total of 9,929 papers were retrieved, of which 20 met the inclusion criteria. Meta-analysis indicated that PM 2.5 exposure was significantly associated with frailty assessed by the frailty index [OR (95% CI): 1.24 (1,11-1.38) per 10 μg/m 3 increment]. Moreover, solid fuel exposures were significantly associated with an increased risk of frailty assessed by the frailty phenotype [OR (95% CI): 1.91 (1.09-3.34)] or the frailty index [OR (95% CI): 1.25 (1.11-1.41)]. Exposure to PM 2.5 and solid fuel increases the risk of frailty. Environmental protection policies and public health measures should be developed to reduce PM 2.5 concentrations. Effective measures, such as improving stoves and using clean fuels, should be taken to reduce indoor air pollution levels., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Olea pollen is considered one of the most critical causes of respiratory allergic disease in the Mediterranean region, contributing to different symptoms in allergic sufferers. Due to the high increase in the prevalence of olive pollen caused by the expansion of cultivated areas especially in the Mediterranean region, the aim of this study was to examine the Olea pollen sampling efficiency between gravimetric and volumetric pollen traps over the period 2018-2022. Our data demonstrated that Olea pollen season start-date was generally recorded earlier with volumetric trap than shown with gravimetric method. Moreover, the peak dates occurred mainly in May for the years studied. Across the entire period, the greatest pollen concentrations were reached in the week 11th and >60 % of the total Olea pollen were already reached after the ninth week of sampling in 2020. In general, the evolution of pollen sampling for both methods demonstrates similar temporal development. Variations in the seasonal pollen integral could be attributed to the elevation above ground level at which the traps were placed, exposure to regional winds, the influence of long-range transported pollen and the mode of pollen transport. The findings indicate that the gravimetric method was equally practical as the volumetric method in estimating airborne pollen., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Background: Noise and air pollution are significant environmental threats with proven adverse health effects. However, the causality between these ambient exposures and disease is still largely unknown. This study aims to provide genetic evidence for this gap and investigates the dual role of inflammatory factors, emphasizing the need for integrated public health strategies., Methods: We included noise and air pollution as exposures, 91 inflammatory factors as mediators, and 26 diseases as outcomes. We explored causal relationships using Mendelian randomization. To ensure the reliability, we screened single nucleotide polymorphisms (SNPs) closely associated with exposure as instrumental variables (IVs), and assessed the pleiotropy and heterogeneity of these IVs., Results: Our results suggest that "Hearing difficulty/problems with background noise" increases the risk of hypertension, bronchitis, and menopause; loud music exposure frequency increases the risk of bronchitis; noisy workplace raises the risk of hypertension, coronary heart disease, narcolepsy, and irritable bowel syndrome; NO 2 increases the risk of myocardial infarction and chronic heart failure; NO x increases the risk of pneumonia and inflammatory diseases of female pelvic organs; and PM 10 increases the risk of myocardial infarction, narcolepsy, and type 2 diabetes; PM 2.5 - 10 increases the risk of developing pneumonia and type 2 diabetes. Furthermore, we found that nine inflammatory factors play a mediating role, of which four play a mediating role in increasing the risk of morbidity and eight play a mediating role in protection against ambient exposures. Finally, we selected SNPs significantly associated with exposure and outcome for enrichment analysis., Conclusions: This study provides the first genetic evidence linking noise and air pollution to various diseases, highlighting the dual mediating role of inflammatory factors. Our findings align with the "One Health" framework, emphasizing the interconnectedness of environmental and human health. Integrated public health strategies considering these complex biological responses are essential for promoting overall well-being., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Autism spectrum disorder (ASD) is a developmental disorder with symptoms that range from social and communication impairments to restricted interests and repetitive behavior and is the 4th most disabling condition for children aged 5-14. Risk factors of ASD are not fully understood. Environmental risk factors are believed to play a significant role in the ASD epidemic. Research focusing on air pollution exposure as an early-life risk factor of autism is growing, with numerous studies finding associations of traffic and industrial emissions with an increased risk of ASD. One of the possible mechanisms linking autism and air pollution exposure is metabolic dysfunction. However, there were no consensus about the key metabolic pathways and corresponding metabolite signatures in mothers and children that are altered by air pollution exposure and cause the ASD. Therefore, we performed a review of published papers examining the metabolomic signatures and metabolic pathways that are associated with either air pollution exposure or ASD risk in human studies. In conclusion, we found that dysregulated lipid, fatty acid, amino acid, neurotransmitter, and microbiome metabolisms are associated with both short-term and long-term air pollution exposure and the risk of ASD. These dysregulated metabolisms may provide insights into ASD etiology related to air pollution exposure, particularly during the perinatal period in which neurodevelopment is highly susceptible to damage from oxidative stress and inflammation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Understanding the factors that drive PM 2.5 concentrations in cities with varying population and land areas is crucial for promoting sustainable urban population health. This knowledge is particularly important for countries where air pollution is a significant challenge. Most existing studies have investigated either anthropogenic or environmental factors in isolation, often in limited geographic contexts; however, this study fills this knowledge gap. We employed a multimethodological approach, using both multiple linear regression models and geographically weighted regression (GWR), to assess the combined and individual effects of these factors across different cities in China. The variables considered were urban built-up area, land consumption rate (LCR), population size, population growth rate (PGR), longitude, and latitude. Compared with other studies, this study provides a more comprehensive understanding of PM 2.5 drivers. The findings of this study showed that PGR and population size are key factors affecting PM 2.5 concentrations in smaller cities. In addition, the extent of urban built-up areas exerts significant influence in medium and large cities. Latitude was found to be a positive predictor for PM 2.5 concentrations across all city sizes. Interestingly, the northeast, south, and southwest regions demonstrated lower PM 2.5 levels than the central, east, north, and northwest regions. The GWR model underscored the importance of considering spatial heterogeneity in policy interventions. However, this research is not without limitations. For instance, international pollution transfers were not considered. Despite the limitation, this study advances the existing literature by providing an understanding of how both anthropogenic and environmental factors, in conjunction with city scale, shape PM 2.5 concentrations. This integrated approach offers invaluable insights for tailoring more effective air pollution management strategies across cities of different sizes and characteristics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

We investigated the effect of individual, joint and fluctuating exposure to air pollution (PM 2.5 , BC, NO 3 - , NH 4 + , OM, SO 4 2- , PM 10 , NO 2 , SO 2 , O 3 ) on glucose metabolisms among prediabetes, and simultaneously explored the modifying effect of lifestyle. We conducted a longitudinal study among prediabetes during 2018-2022. Exposure windows within 60-days moving averages and their variabilities were calculated. FBG, insulin, HOMA-IR, HOMA-B, triglyceride glucose index (TyG), glucose insulin ratio (GI) and allostatic load of glucose homeostasis system (AL-GHS) was included. Linear mixed-effects model and BKMR were adopted to investigate the individual and overall effects, respectively. We also explored the preventive role of lifestyle. Individual air pollutant was associated with increased FBG, insulin, HOMA-IR, HOMA-B, TyG, and decreased GI. People with FBG ≥6.1 mmol/L were more susceptible. Air pollutants mixture were only associated with increased HOMA-B, and constituents have the highest group-PIP. Air pollutants variation also exert harmful effect. We observed similar diabetic effect on AL-GHS. Finally, the diabetic effect of air pollutants disappeared if participants adopt a favorable lifestyle. Our findings highlighted the importance of comprehensively assessing multiple air pollutants and their variations, focusing on metabolic health status in the early prevention of T2D, and adopting healthy lifestyle to mitigate such harmful effect., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Understanding and quantifying the influences and contributions of air pollution emissions on water quality variations is critically important for surface water quality protection and management. To address this, we created a five-year daily data matrix of six water quality indicators-permanganate index (COD Mn ), NH 3 -N, pH, turbidity, conductivity, and dissolved organic matter (DOM)-and six air pollution indicators-O 3 , CO, NO 2 , SO 2 , 2.5 μm particulate matter (PM 2.5 ), and inhalable particles (PM 10 )-using data from seven national monitoring stations along the world's longest water-diversion project, the Middle Route of the South-to-North Water Diversion Project in China (MR-SNWD). Multivariate techniques (Mann-Kendall, Spearman's correlation, lag correlation, and Generalized Additive Models [GAMs]) were applied to examine the nonlinear relationships and lag effects of air pollution on water quality. Air pollution and water quality exhibited marked spatial heterogeneity along the MR-SNWD, with all water quality parameters meeting Class I or II national standards and the air pollution indicators exceeding those thresholds. Except for COD Mn and DOM, the other water quality and air pollution indicators exhibited significant seasonal differences. Air pollution exhibited significant lag effects on water quality at the northern stations, with NO 2 , SO 2 , PM 2.5 , and PM 10 being highly correlated with changes in pH, with an average lag of 17 d. Based on the GAMs, lag effects enhanced the significant nonlinear relationships between air pollution and water quality, increasing the average deviance explained for COD Mn , NH 3 -N, and pH by 93%, 24%, and 41%, respectively. These findings provide a scientific basis for protecting water quality along the long-distance inter-basin water-diversion project under anthropogenic air pollution., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Exposure to air pollution has been associated with increased risks of cardiopulmonary diseases, cancer, and mortality, whereas residing near green spaces may reduce the risks. However, limited research explores their combined effect on oxidative stress., Methods: A total of 251 participants with multi-time measurements were included in the longitudinal-designed study. Personal gaseous air pollutants (CO, NO, NO 2 , and O 3 ,) and particulate pollution (PM 1 , PM 2.5 , and PM 10 ) were measured and followed in two 7-day windows while ambient exposure levels and urine samples were collected simultaneously. Participants' Normalized Difference Vegetation Index (NDVI) was estimated and used to represent greenness exposure. Urinary oxidative stress biomarkers include free malondialdehyde (MDA), total MDA, and 8-hydroxydeoxyguanosine (8-OHdG). Linear mixed-effects models were used to independently and jointly estimate the associations of greenness and air pollution with oxidative stress biomarkers., Results: We found consistent positive associations of personal ozone (O 3 ) exposure with 8-OHdG percent changes, and this association was modified by gender and outdoor activity frequency. Consistent positive associations of personal lag 2-day carbon monoxide (CO) exposure with the percent changes of the three oxidative stress biomarkers were significant. We additionally observed that individuals who lived in greener areas had lower levels of urinary-free and total MDA. Participants in the highest NDVI tertile had 0.38 and 0.46 lower free and total MDA levels, [95 % CI: (-0.70, -0.05) and (-0.78, -0.13)], compared to the lowest NDVI tertile. There was also evidence indicating the modification effects by area, education, and outdoor activity frequency on associations between NDVI exposure and creatinine adjusted free MDA (all P for interaction  < 0.05). Additional greenness modification effects on personal O 3 exposure with urinary 8-OHdG was observed., Conclusion: Our study provides biological evidence of the modification effect of the built environment on the impact of air pollution., Competing Interests: Declaration of competing interest The authors declare they have no conflicts of interest related to this work to disclose., (Copyright © 2024. Published by Elsevier B.V.)

The influence of air pollution on dynamic changes in clinical state from healthy to atrial fibrillation (AF), further AF-related complications and ultimately, death are unclear. We aimed to investigate the relationships between air pollution and the occurrence and progression trajectories of AF. We retrieved 442,150 participants free of heart failure (HF), myocardial infarction (MI), stroke and dementia at baseline from UK Biobank. Exposures to air pollution for each transition stage were estimated at the geocoded residential address of each participant using the bilinear interpolation approach. The outcomes were incident AF, complications, and death. Multi-stage models were used to evaluate the associations between air pollution and dynamic progression of AF. Over a 12.6-year median follow-up, a total of 21,670 incident AF patients were identified, of whom, 4103 developed complications and 1331 died. PM 2.5 , PM 10 , NO x and NO 2 were differentially positively associated, while O 3 was negatively associated with risks of progression trajectories of AF. PM 2.5 exposure was significantly associated with an increased risk of progression. The associations of PM 2.5 , PM 10 , NO x , and NO 2 on incident AF were generally more pronounced compared to other transitions. The cumulative transition probabilities were generally higher in individuals with higher exposure levels of PM 2.5 , PM 10 , NO x , and NO 2 and lower exposure to O 3 . Air pollution could potentially have a role in increasing the risk of both the occurrence and progression of AF, emphasizing the significance of air pollution interventions in both the primary prevention of AF and the management of AF-related outcomes., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: GYHL: Consultant and speaker for BMS/Pfizer, Boehringer Ingelheim, Daiichi-Sankyo, Anthos. No fees are received personally. GYHL is a National Institute for Health and Care Research (NIHR) Senior Investigator and co-principal investigator of the AFFIRMO project on multimorbidity in AF, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 899871. Other authors declare no competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The increase in the frequency and severity of global wildfires has been largely influenced by climate change and land use changes. From February 2 to 6, 2024, central Chile experienced its most devastating wildland-urban interface wildfire in history, severely impacting the Valparaíso region. This catastrophic event, which led to extensive forest destruction, the loss of thousands of homes, and over a hundred human fatalities, directly impacted the area surrounding the campus of Federico Santa María Technical University. In that period, an air quality monitoring campaign was set up on the campus to measure black carbon (BC) and particulate matter (PM) during the wildfire season. The monitoring station was located directly within the smoke plume, allowing for the collection of unprecedented air quality data. Extremely high concentrations of BC at 880 nm were reported during the wildfires, with a daily mean (±σ) of 14.83 ± 19.52 μg m -3 . Peak concentrations measured at 880 nm and 375 nm reached 812.89 μg m -3 and 1561.24 μg m -3 , respectively. The maximum daily mean BC concentrations at these wavelengths were 55 and 99 times higher, respectively, compared to the pre-event period. The mean Ångström absorbing coefficient during the event was 1.66, indicating biomass burning as the primary BC source, while the maximum BC/PM 2.5 ratio (at 375 nm) reached 57 %. From February 2 to 5, 2024, PM concentrations exceeded the Chilean air quality standard by 82 % and 198 % for coarse and fine particles, respectively. These levels are 4.7 and 6.0 times higher than the World Health Organization's recommendations. These elevated concentrations persisted for up to three days after the fire was extinguished. This study provides unique evidence of the rapid deterioration of regional air quality during a wildfire event using in situ measurements, serving as a stark reminder of the far-reaching consequences of a warming climate., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Recently pilot published city-level air quality health index (AQHI) provides a useful tool for communicating short-term health risks of ambient air pollution, but fails to account for intracity spatial heterogeneity in exposure and associated population health impacts. This study aims to develop the intracity spatiotemporal AQHI (ST-AQHI) via refined air pollution-related health risk assessments., Methods: A three-stage analysis was conducted through integrating province-wide death surveillance data and high-resolution gridded estimates of air pollution and climate factors spanning 2016-2019 in Jiangsu Province, eastern China. First, an individual-level case-crossover design was employed to quantify the short-term risk of nonaccidental mortality associated with residential exposure to individual pollutant (i.e., PM 2.5 , NO 2 , O 3 , and SO 2 ). Second, we accumulated and scaled the excess risks arising from multiple pollutants to formulate daily gridded ST-AQHI estimates at 0.1° × 0.1°, dividing exposure-related risks into low (0-3), moderate (4-6), high (7-9), and extreme high (10+) levels. Finally, the effectiveness of ST-AQHI as composite risk communication was validated through checking the dose-response associations of individual ST-AQHI exposure with deaths from nonaccidental and major cardiopulmonary causes via repeating case-crossover analyses., Results: We analyzed a total of 1,905,209 nonaccidental death cases, comprising 785,567 from circulatory diseases and 247,336 from respiratory diseases. In the first-stage analysis, for each 10-μg/m 3 rise in PM 2.5 , NO 2 , O 3 , and SO 2 exposure at lag-01 day, population risk of nonaccidental death was increased by 0.8% (95% confidence interval: 0.7%, 0.9%), 1.9% (1.7%, 2.0%), 0.4% (0.3%, 0.5%), and 4.1% (3.7%, 4.5%), respectively. Spatiotemporal distribution of ST-AQHI exhibited a consistent declining trend throughout the study period (2016-2019), with annual average ST-AQHI decreasing from 5.2 ± 1.3 to 4.0 ± 1.0 and high-risk days dropping from 15.8% (58 days) to 1.6% (6 days). Exposure associated health risks showed great intracity- and between-city heterogeneities. In the validation analysis, ST-AQHI demonstrated approximately linear, threshold-free associations with multiple death events from nonaccidental and major cardiopulmonary causes, suggesting excellent performance in predicting exposure-related health risks. Specifically, each 1-unit rise in ST-AQHI was significantly associated with an excess risk of 2.0% (1.8%, 2.1%) for nonaccidental mortality, 2.3% (2.1%, 2.6%) for overall circulatory mortality, and 2.7% (2.3%, 3.1%) for overall respiratory mortality, as well as 1.7%-3.0% for major cardiopulmonary sub-causes., Conclusions: ST-AQHI developed in this study performed well in predicting intracity spatiotemporal heterogeneity of death risks related to multiple air pollutants, and may hold significant practical importance in communicating air pollution-related health risks to the public at the community scales., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Cities are treated as global methane (CH 4 ) emission hotspots and the monitoring of atmospheric CH 4 concentration in cities is necessary to evaluate anthropogenic CH 4 emissions. However, the continuous and in-situ observation sites within cities are still sparsely distributed in the largest CH 4 emitter as of China, and although obvious seasonal variations of atmospheric CH 4 concentrations have been observed in cities worldwide, questions regarding the drivers for their temporal variations still have not been well addressed. Therefore, to quantify the contributions to seasonal variations of atmospheric CH 4 concentrations, year-round CH 4 concentration observations from 1st December 2020 to 30th November 2021 were conducted in Hangzhou megacity, China, and three models were chosen to simulate urban atmospheric CH 4 concentration and partition its drivers including machine learning based Random Forest (RF) model, atmospheric transport processes based numerical model (WRF-STILT), and regression analysis based Multiple Linear Regression (MLR) model. The findings are as follows: (1) the atmospheric CH 4 concentration showed obvious seasonal variations and were different with previous observations in other cities, the seasonality were 5.8 ppb, 21.1 ppb, and 50.1 ppb between spring-winter, summer-winter and autumn-winter, respectively, where the CH 4 background contributed by -8.1 ppb, -44.6 ppb, and -1.0 ppb, respectively, and the CH 4 enhancements contributed by 13.9 ppb, 65.7 ppb, and 51.1 ppb. (2) The RF model showed the highest accuracy in simulating CH 4 concentrations, followed by MLR model and WRF-STILT model. (3) We further partition contributions from different factors, results showed the largest contribution was from temperature-induced increase in microbial process based CH 4 emissions including waste treatment and wetland, which ranged from 38.1 to 76.3 ppb when comparing different seasons with winter. The second largest contribution was from seasonal boundary layer height (BLH) variations, which ranged from -13.4 to -6.3 ppb. And the temperature induced seasonal CH 4 emission and enhancement variations were overwhelming BLH changes and other meteorological parameters., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: This study aimed to investigate the influence of exposure to ambient fine particulate matter (PM 2.5 ) and its components during pregnancy on the prevalence of preterm birth (PTB). Additionally, we sought to identify the susceptible exposure window. Furthermore, we explored the potential mediating role of blood analysis and a comprehensive metabolic panel in the association between pollutant exposure and PTB incidence., Methods: This birth cohort study recruited 139 participants with PTB outcomes and 1713 controls from Fujian Maternal and Child Health Hospital between January 2021 and June 2023. Sociodemographic characteristics and clinical treatment data during participants' first pregnancies were collected. The exposure levels to pollutants during pregnancy were estimated via a combined geographic-statistical model utilising satellite remote sensing data. The distributional lag nonlinear modelling was employed to assess associations between pollutant exposure during pregnancy and the prevalence of PTB. Weighted quantile regression was used to identify key components associated with PM 2.5 and PTB during pregnancy. Additionally, a mediating effect analysis was conducted to evaluate the role of blood analysis. The metabolic profile was used to screen for differentially abundant metabolites associated with PTB and explore their relative expression in relation to air pollutants and PTB incidence., Results: Following the adjustment for potential confounding variables, the mean weekly susceptibility windows for PM 2.5 were identified as 7-10, 16-19, and 22-28 weeks; 8-10, and 15-19 weeks for inorganic sulfate; 6-10, and 15-28 weeks for nitrate; 6-12, and 15-28 weeks for ammonium (NH 4 + ); and 7-9, 18-20, and 22-36 weeks for organic matter. During mixed exposure to PM 2.5 components, the key component is NH 4 + . In the mixed exposure to PM 2.5 components, NH 4 + emerged as a key contributor. The results of the mediation analysis revealed that haemoglobin played a mediating role, accounting for 21.53 % of the association between exposure to environmental pollutants and the prevalence of PTB. It is noteworthy that, no mediating effects were observed for the other variables. Furthermore, non-targeted metabolomics identified 17 metabolites associated with PTB. Among these factors, hydrogen phosphate may impact metabolic pathways such as oxidative phosphorylation, influencing the risk of PTB. The interplay between environmental pollutants and metabolites, particularly through oxidative phosphorylation pathways, may contribute to PTB incidence., Conclusions: The evidence indicates that exposure to PM 2.5 and its components during pregnancy were a significant risk factor for PTB. Notably, specific weekly exposure windows were identified for pollutants during pregnancy. Among the PM 2.5 components, NH 4 + exhibited the most substantial weight in the association analysis between exposure to the mixture of components and PTB. Furthermore, our mediation analysis revealed that haemoglobin serves as a partial mediator in the relationship between exposure to pollutants during pregnancy and the prevalence of PTB. Additionally, maternal serum metabolic profiles differed between the preterm and control groups. Notably, a combined effect involving hydrogen phosphate and mixed exposure to PM 2.5 fractions further contributed to the development of PTB. Oxidative phosphorylation pathways may play pivotal roles in this intricate association., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In the southwestern United States, the frequency of summer wildfires has elevated ambient PM 2.5 concentrations and rates of adverse birth outcomes. Notably, hypertensive disorders in pregnancy (HDP) constitute a significant determinant associated with maternal mortality and adverse birth outcomes. Despite the accumulating body of evidence, scant research has delved into the correlation between chemical components of wildfire PM 2.5 and the risk of HDP. Derived from data provided by the National Center for Health Statistics, singleton births from >2.68 million pregnant women were selected across 8 states (Arizona, AZ; California, CA, Idaho, ID, Montana, MT; Nevada, NV; Oregon, OR; Utah, UT, and Wyoming, WY) in the southwestern US from 2001 to 2004. A spatiotemporal model and a Goddard Earth Observing System chemical transport model were employed to forecast daily concentrations of total and wildfire PM 2.5 -derived exposure. Various modeling techniques including unadjusted analyses, covariate-adjusted models, propensity-score matching, and double robust typical logit models were applied to assess the relationship between wildfire PM 2.5 exposure and gestational hypertension and eclampsia. Exposure to fire PM 2.5 , fire-sourced black carbon (BC) and organic carbon (OC) were associated with an augmented risk of gestational hypertension (OR PM2.5  = 1.125, 95 % CI: 1.109,1.141; OR BC  = 1.247, 95 % CI: 1.214,1.281; OR OC  = 1.153, 95 % CI: 1.132, 1.174) and eclampsia (OR PM2.5  = 1.217, 95 % CI: 1.145,1.293; OR BC  = 1.458, 95 % CI: 1.291,1.646; OR OC  = 1.309, 95 % CI: 1.208,1.418) during the pregnancy exposure window with the strongest effect. The associations were stronger that the observed effects of ambient PM 2.5 in which the sources primarily came from urban emissions. Social vulnerability index (SVI), education years, pre-pregnancy diabetes, and hypertension acted as effect modifiers. Gestational exposure to wildfire PM2.5 and specific chemical components (BC and OC) increased gestational hypertension and eclampsia risk in the southwestern United States., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Many studies have drawn attention to the associations of oxygenated polycyclic aromatic hydrocarbons (OPAHs) with harmful health effects, advocating for their systematic monitoring alongside simple PAHs to better understand the aerosol carcinogenic potential in urban areas. To address this need, this study conducted an extensive PM 2.5 sampling campaign in Athens, Greece, at the Thissio Supersite of the National Observatory of Athens, from December 2018 to July 2021, aiming to characterize the levels and variability of polycyclic aromatic compounds (PACs), perform source apportionment, and assess health risk. Cumulative OPAH concentrations (Σ-OPAHs) were in the same range as Σ-PAHs (annual average 4.2 and 5.6 ng m -3 , respectively). They exhibited a common seasonal profile with enhanced levels during the heating seasons, primarily attributed to residential wood burning (RWB). The episodic impact of biomass burning was also observed during a peri-urban wildfire event in May 2021, when PAH and OPAH concentrations increased by a factor of three compared to the monthly average. The study period also included the winter 2020-2021 COVID-19 lockdown, during which PAH and OPAH levels decreased by >50 % compared to past winters. Positive matrix factorization (PMF) source apportionment, based on a carbonaceous aerosol speciation dataset, identified PAC sources related to RWB, local traffic (gasoline vehicles) and urban traffic (including diesel emissions), as well as an impact of regional organic aerosol. Despite its seasonal character, RWB accounted for nearly half of Σ-PAH and over two-thirds of Σ-OPAH concentrations. Using the estimated source profiles and contributions, the source-specific carcinogenic potency of the studied PACs was calculated, revealing that almost 50 % was related to RWB. These findings underscore the urgent need to regulate domestic biomass burning at a European level, which can provide concrete benefits for improving urban air quality, towards the new stricter EU standards, and reducing long-term health effects., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

This study investigates the stability and sustainability of carbon decoupling in urban agglomerations across China, where the strong coupling between economic growth and carbon emissions poses significant challenges. Despite efforts in energy conservation and emission reduction, urban agglomerations have seen unsatisfactory results. By analyzing the real-pseudo decoupling states in 19 urban agglomerations from 2007 to 2020, the objective of this study is to identify the type and trend characteristics of carbon decoupling traps and to propose solution paths for maintaining decoupling stability. Major Findings: (1) The decoupling state exhibits volatility and instability in urban agglomerations, making them susceptible to decoupling traps. (2) Most urban agglomerations remain un-decoupled, with a few cities achieving real decoupling and gradually shifted from northeast to southeast, while pseudo-decoupling and un-decoupled cities consistently cluster in the southwest and northwest regions. (3) Real-pseudo decoupling is driven by a combination of endogenous and exogenous factors, with energy structure, population density, and environmental regulation intensity emerging as pivotal influencers. (4) Geographical factors exhibit both commonalities and variations in their impact on real-pseudo decoupling. By identifying real-pseudo decoupling states and their driving factors, this study proposes strategic solution paths to overcome carbon constraints and achieve stable decoupling in urban agglomerations, contributing to the broader goals of sustainable economic and environmental development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The global apprehension regarding the ubiquitous presence of microplastics (MPs) and their associated health risks underscore a significant challenge. However, our understanding on the occurrence and characteristics of this emerging class of pollutants in the different environmental compartments remains limited. For instance, despite housing approximately 20-25% of the global population, the evidence of the atmospheric MPs in Indian Subcontinent is exceedingly rare. Hence, we for the first-time present data on the depositional flux, chemical composition, morphological features of the atmospheric MPs collected from the foothills of Indian Himalayas. The total number of MPs for the collected samples ranged from 65 to 752 particles, with an average of 317 ± 171 particles count. The average flux of atmospheric MPs was 2256 ± 1221 particles/m 2 /day and varied significantly from 462 particles/m 2 /day to 5346 particles/m 2 /day. The highest deposition (5346 particles/m 2 /day) of atmospheric MPs was recorded during the 3rd week of sampling, coinciding with the Diwali festival. Based on the visual characteristics, we determined that the size of MPs ranged from 67 to 2320 μm, with a predominant presence of smaller particles (<1200 μm), primarily composed of fragments and films/sheets. Raman spectroscopy indicated that the analyzed MPs were mainly composed of 4 different polymer types, including PE (46.8 ± 7.2 %), PP (20.9 ± 7.4 %), PS (15.6 ± 3.8 %) and PET (16.7 ± 9.9 %). We further highlighted the extent to which climate variables control the deposition of atmospheric MPs in this urban conglomerate located in the foothills of Himalayas. Our Lagrangian parcel tracking approach showed that the greater frequencies are of local origin and clustered near to the studied region. We also speculate that atmospheric microplastics can be transported along the westerly winds. Though we did not observe any significant relation (p > 0.05) between meteorological parameters and the quantity of atmospheric MPs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Introduction: Air pollution and transportation noise pollution has been linked to gastrointestinal (GI) diseases, but their relationship remains unclear., Methods: We extracted the significantly modulated genes and CpG sites related to air pollution (PM2.5, PM10, and NOx) and transportation noise pollution (aircraft, railway, and traffic road noise) from previous published studies. Genome-wide methylation analysis and colocalization analysis with these CpG sites and GWAS data of GI diseases were performed to disentangle the relationship between pollution-related blood DNA methylation (DNAm) alterations and GI diseases risk. Summary-based Mendelian randomization (SMR) analysis assessed the impact of pollution-related genes on GI diseases risk across methylation, gene expression, and protein levels. Enrichment analysis investigated the implicated biological pathways and immune cell types., Results: DNAm at cg00227781 [CD300A] (modulated by NOx exposure) and cg19215199 [ZMIZ1] (modulated by PM2.5 exposure) was significantly linked to increased noninfective enteritis and colitis risk, while cg08500171 [BAT2] (modulated by NOx exposure) is significantly associated with an increased gastroesophageal reflux disease (GERD) risk. Colocalization analysis provides strong evidence supporting a shared causal variant between these associations. Multi-omics levels SMR analysis revealed that pollution-modulated lower DNAm at 5 specific CpG sites were associated with increased expression of 4 genes (IL21R, EVPL, SYNGR1, and WDR46), subsequently increasing the risk of GERD, ulcerative colitis, and gastric ulcer. 7 circulating proteins coded by pollution-modulated genes were observed to be associated with 6 GI diseases risk. Enrichment analysis implicates immune and inflammatory responses, MAPK signaling, and telomere maintenance in these pollution-induced effects., Conclusion: We identified potential links between air and transportation noise pollution-related gene methylation, expression, and protein abundance with GI diseases risk, possibly revealing new therapeutic targets., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ying-Xuan Chen reports article publishing charges was provided by National Natural Science Foundation of China. Ying-Xuan Chen reports article publishing charges was provided by Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The aim of this study is to assess the spatiotemporal variation, sources, and health impacts of the carbonyl compounds (carbonyls) in Ho Chi Minh City (HCMC), the third-most populous city in Southeast Asia. Sampling was conducted according to the US.EPA Method TO-11A, from 2012 to 2016 in both the dry and the rainy seasons at twelve sites. The result shows that the carbonyl mixing ratios are high when compared to typical cities. Formaldehyde, acetaldehyde, and acetone are the most abundant carbonyls together accounting for 89% of the measured carbonyls. The carbonyl mixing ratio in rainy (46.0 ± 32.2 ppb) is about twofold higher than that in dry (23.7 ± 10.3 ppb). An inverse distance weighting method was adopted to map the spatial distribution of carbonyls across the city. The result shows that the carbonyl levels tended to be high in the city center. Three carbonyl sources are resolved by the PCA/APCS method: industrial sources and solvent usage (54%), vehicle exhausts (24%), cooking emissions (11%). Both lifetime cancer risk (LCR) and non-cancer hazard index (HIs) were calculated to estimate the health impacts on the community due to inhalation exposure to current carbonyl levels. The LCR values vary from 5.31 × 10 -6 to 5.75 × 10 -5 for formaldehyde higher than those of 6.61 × 10 -7 to 1.36 × 10 -5 for acetaldehyde, which mostly exceeded the US.EPA recommendation for five age groups. The hazard quotient values are 12.0 to 68.4 for acrolein, 0.44 to 2.84 for acetaldehyde, 0.55 to 1.85 for formaldehyde, and 0.13 to 0.89 for propionaldehyde., Competing Interests: Declarations Ethical approval This study did not involve human or animal subjects and therefore did not require ethical approval. All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors.” as found in the Instructions for Authors. Consent to participate Not applicable, as this study did not involve human subjects. Consent for publication Not applicable. Conflict of interest The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Many cities have promoted decarbonized transportation modes to mitigate climate change, reduce air pollution and promote healthy behaviors. However, cyclists may be particularly exposed to higher concentrations of on-road air pollutants than other commuters due to their proximity to traffic, higher breathing rates, and prolonged commutes. In addition, there is scarce information analyzing the geographic exposure levels of cyclists in medium-sized urban agglomerations of Latin American cities. We aimed to assess cyclists' exposure to PM 2.5 at the spatio-temporal level using low-cost sensors in the Mendoza Metropolitan Area, Argentina. We calculated PM 2.5 inhalation doses (IDs) for different routes with distinctive characteristics, considering different age ranges and gender of cyclists. The dose was represented as temporally and spatially disaggregated exposure maps, one of the first to represent it in Latin America using this method. All link types analyzed exhibited significant differences in PM 2.5 concentrations, although the most frequent concentrations were less than 5 µg m -3 , with secondary peaks of 6.5 and 9 µg m -3 . As expected, ID increases with age and is greater in males than in females. Our findings further reinforce the fact that route choice (busy vs. quiet) and time of day (peak vs. off-peak) appreciably affect the pollutant exposure of cyclists. All these results could be helpful in the selection of alternative cycling routes with lower PM 2.5 at different hours of day but also enable us to investigate further implications of exposure to PM 2.5 for the health of urban bicycle commuters., Competing Interests: Declarations Ethical approval This paper has no ethical issues (not working on animal or human issues). Consent to participate Not applicable. Consent to publish All of the authors have read and approved the paper for publication. We confirm that this manuscript has not been published previously and is not being considered by any other peer-reviewed journal. Competing interests The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Mercury (Hg), a ubiquitous atmospheric trace metal posing serious health risks, originates from natural and anthropogenic sources. India, the world's second-largest Hg emitter and a signatory to the Minamata Convention, is committed to reducing these emissions. However, critical gaps exist in our understanding of the spatial and temporal distribution of Hg across the vast Indian subcontinent due to limited observational data. This study addresses this gap by employing the GEOS-Chem model with various emission inventories (UNEP2010, WHET, EDGAR, STREETS, and UNEP2015) to simulate Hg variability across the Asian domain, with a specific focus on India from 2013 to 2017. Model performance was evaluated using ground-based GMOS observations and available literature data. Emission inventory performance varied across different observational stations. Hence, we employed ensemble results from all inventories. The maximum relative bias for Total Gaseous Mercury (TGM) and Gaseous Elemental Mercury (GEM; Hg 0 ) concentrations is about ±20%, indicating simulations with sufficient accuracy. Total Hg wet deposition fluxes are highest over the Western Ghats and the Himalayan foothills due to higher rainfall. During the monsoon, the Hg wet deposition flux is about 65.4% of the annual wet deposition flux. Moreover, westerly winds cause higher wet deposition in summer over Northern and Eastern India. Total Hg dry deposition flux accounts for 72-74% of total deposition over India. Hg 0 dry deposition fluxes are higher over Eastern India, which correlates strongly with the leaf area index. Excluding Indian anthropogenic emissions from the model simulations resulted in a substantial decrease (21.9% and 33.5%) in wet and total Hg deposition fluxes, highlighting the dominant role of human activities in Hg pollution in India.

The elevated concentration of suspended particulate matter in the atmosphere is closely associated with the morbidity and mortality of human diseases. In northwest China, fine particulate matter pollution has persistently affected local health, production, and daily life due to climatic factors and economic development. This paper first examines the distribution and variation of PM 2.5 concentrations in northwest China from 2013 to 2022 to understand changes in PM 2.5 exposure levels, as well as their associated health and economic benefits since the implementation of the "Air Pollution Prevention and Control Action Plan." Second, we estimated the number of premature deaths in the research region under various control scenarios based on PM 2.5 concentrations recorded in 2013 and 2021, utilizing existing epidemiological studies for reference. Finally, we calculated the financial benefits resulting from variations in PM 2.5 concentrations within this region. Results indicated that (1) from 2013 to 2022, the Tarim Basin exhibited the highest PM 2.5 concentration among all studied areas, while northern Xinjiang and Qinghai Province had notably lower levels. Additionally, wintertime concentrations were consistently higher than those observed during summer months; however, there has been a general trend toward reduced PM 2.5 content over the past decade. (2) A significant inverse relationship was found between premature mortality rates and PM 2.5 concentrations; specifically, fluctuations in PM 2.5 levels exerted a more pronounced impact on premature deaths attributable to cardiovascular diseases compared to respiratory conditions within this northwest region. (3) Males were disproportionately affected by increases in PM 2.5 concentration relative to females-experiencing two to three times more early fatalities than their female counterparts did. (4) Over a 9-year period within this research area, fiscal benefits derived from interventions targeting PM 2.5 treatment increased by approximately 891 million yuan., Competing Interests: Declarations Ethics approval Unethical actions were not made. Consent to participate All authors gave consent to participate in this publication. Consent for publication All authors gave consent for publication in the International Journal of Environmental Research. Conflict of interest The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Environmental exposures and community characteristics have been linked to accelerated lung function decline in people with cystic fibrosis (CF), but geomarkers, the measurements of these exposures, have not been comprehensively evaluated in a single study. To determine which geomarkers have the greatest predictive potential for lung function decline and pulmonary exacerbation (PEx), a retrospective longitudinal cohort study was performed using novel Bayesian joint covariate selection methods, which were compared with respect to PEx predictive accuracy. Non-stationary Gaussian linear mixed effects models were fitted to data from 151 CF patients aged 6-20 receiving care at a CF Center in the midwestern US (2007-2017). The outcome was forced expiratory volume in 1 s of percent predicted (FEV1pp). Target functions were used to predict PEx from established criteria. Covariates included 11 routinely collected clinical/demographic characteristics and 45 geomarkers comprising 8 categories. Unique covariate selections via four Bayesian penalized regression models (elastic-net, adaptive lasso, ridge, and lasso) were evaluated at both 95 % and 90 % credible intervals (CIs). Resultant models included one to 6 geomarkers (air temperature, percentage of tertiary roads outside urban areas, percentage of impervious nonroad outside urban areas, fine atmospheric particulate matter, fraction achieving high school graduation, and motor vehicle theft) representing weather, impervious descriptor, air pollution, socioeconomic status, and crime categories. Adaptive lasso had the lowest information criteria. For PEx predictive accuracy, covariate selection from the 95 % CI elastic-net had the highest area under the receiver-operating characteristic curve (mean ± standard deviation; 0.780 ± 0.026) along with the 95 % CI ridge and lasso methods (0.780 ± 0.027). The 95 % CI elastic-net had the highest sensitivity (0.773 ± 0.083) while the 95 % CI adaptive lasso had the highest specificity (0.691 ± 0.087), suggesting the need for different geomarker sets depending on monitoring goals. Surveillance of certain geomarkers embedded in prediction algorithms can be used in real-time warning systems for PEx onset., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Epidemiological evidence regarding the association between air pollutants and cardiopulmonary disease, mortality in individuals with preserved ratio impaired spirometry (PRISm), and their combined effects remains unclear., Methods: We followed 36,149 participants with PRISm in the UK Biobank study. Annual concentrations of PM 2.5 , PM 10 , NO 2 , NO x , and SO 2 at residential addresses were determined using a bilinear interpolation method, accounting for address changes. A multistate model assessed the dynamic associations between air pollutants and cardiopulmonary diseases and mortality in PRISm. Quantile g-computation was used to investigate the joint effects of air pollutants., Results: Long-term exposure to PM 2.5 , PM 10 , NO 2 , NO x , and SO 2 was significantly associated with the risk of cardiopulmonary disease in PRISm. The corresponding hazard ratios (HRs) [95 % confidence intervals (95 % CIs)] per interquartile range (IQR) were 1.49 (1.43, 1.54), 1.52 (1.46, 1.57), 1.34 (1.30, 1.39), 1.30 (1.26, 1.34), and 1.44 (1.41, 1.48), respectively. For mortality, the corresponding HRs (95 % CIs) per IQR were 1.36 (1.25, 1.47), 1.35 (1.24, 1.46), 1.27 (1.18, 1.36), 1.23 (1.15, 1.31), and 1.29 (1.20, 1.39), respectively. In PRISm, quantile g-computation analysis demonstrated that a quartile increase in exposure to a mixture of all air pollutants was positively associated with the risk of cardiopulmonary disease and mortality, with HRs (95 % CIs) of 1.84 (1.76, 3.84) and 1.45 (1.32, 1.57), respectively., Conclusion: Long-term individual and joint exposure to air pollutants (PM 2.5 , PM 10 , NO 2 , NO x , and SO 2 ) might be an important risk factor for cardiopulmonary disease and mortality in high-risk populations with PRISm., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In many rapidly urbanizing and industrializing countries, atmospheric pollution causes severe environmental problems and compromises the health of humans and ecosystems. Atmospheric emissions, which encompass gases and particulate matter, can be transported back to the earth's surface through atmospheric deposition. Atmospheric deposition supplies chemical species that can serve as nutrients and/or toxins to aquatic ecosystems, resulting in wide-ranging responses of aquatic organisms. Among the aquatic organisms, phytoplankton is the basis of the aquatic food web and is a key player in global primary production. Atmospheric deposition alters nutrient availability and thus influences phytoplankton species abundance and composition. This review provides a comprehensive overview of the physiological responses of phytoplankton resulting from the atmospheric deposition of trace metals, nitrogen-containing compounds, phosphorus-containing compounds, and sulfur-containing compounds in particulate matter into aquatic ecosystems. Knowledge gaps and critical areas for future studies are also discussed., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Theodora Nah reports financial support was provided by State Key Laboratory of Marine Pollution (SKLMP). Theodora Nah reports financial support was provided by Guangdong Basic and Applied Basic Research Fund Committee. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The present study is focused on investigating the heavy/toxic metals (Al, Ni, Cr, Pb, Cu, As, Mn, and Zn) of PM 2.5 and assessing their associated human health risks. During the study period (July 2022 to July 2023), the PM 2.5 samples were collected from two distinct sites in Faridabad (92 samples from site 1 and 85 samples from site 2). In this study, the US EPA's Risk Assessment Guidance for Superfund (RAGS) was followed to evaluate the human health risk associated with PM 2.5 -bound heavy elements. The annual average of PM 2.5 concentrations was 108 ± 16 µg m⁻ 3 at site 1 and 154 ± 11 µg m⁻ 3 at site 2, approximately three to four times higher than the national ambient air quality standards (annual, 40 µg m -3 ). The analysis of enrichment factors (EFs) for the elements Cr, As, Zn, Cu, Mn, Pb, and Ni indicates that the heavy elements associated with PM 2.5 primarily originate from anthropogenic sources. The application of the conditional bivariate probability function (CBPF) model for Faridabad revealed local pollution sources contributing to elevated mass concentrations at the receptor site from the southern (S), northwestern (NW), northeastern (NE), southwestern (SW), and southeastern (SE) regions. Furthermore, positive matrix factorization (PMF) analysis identified the predominant sources of PM 2.5 -bound heavy elements as industrial emissions (41%), vehicular emissions (34%), and combustion processes (25%). After a thorough assessment of health hazards, Cr appeared as a significant carcinogenic risk factor. Children with elevated hazard quotient (HQ) values for Mn and Cr indicated non-carcinogenic health problems. Ultimately, this analysis reinforces the necessity for rigorous monitoring and intervention to safeguard public health from the potentially harmful effects of heavy elements., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Black carbon (BC) is a pollutant produced by the combustion of fossil fuels and biomass fuels, which has a huge impact on regional climate, atmospheric environment, and human health. In this study, based on MERRA-2 reanalysis data and ground-based observation data, the Mann-Kendall (MK) test and random forest (RF) model were used to explore the spatiotemporal variation characteristics of atmospheric BC concentration in Xinjiang, China and its correlation with meteorological and environmental covariates in 2010-2022. The results showed that the use of MERRA-2 reanalysis data to explore the spatiotemporal variation characteristics of BC concentration in Xinjiang had high reliability (relative average deviation (RAD) = 0.65). From 2010 to 2022, the annual average concentration of atmospheric BC in Xinjiang was 195.40 ± 15.55 ng/m 3 , and the multi-year average change rate was -0.05%. The winter season had the highest atmospheric BC concentration (145.52 ± 39.31 ng/m 3 ), followed by autumn (124.95 ± 28.82 ng/m 3 ), spring (74.05 ± 9.96 ng/m 3 ), and summer (73.41 ± 5.69 ng/m 3 ). The atmospheric BC concentration had a significant spatial variation, showing two high-BC-concentration areas on the northern slope of the Tianshan Mountains (centered on Urumqi-Changji-Shihezi region) and the urban agglomeration around the Tarim Basin (centered on Kashgar). The RF model analysis showed that meteorological factors including snow depth, surface temperature, and humidity as well as environmental factors including NO 2 , PM 10 , and SO 2 were the main factors affecting the BC concentration. This work is of great significance for clarifying the accumulation and spatial distribution characteristics of atmospheric BC in northwest China and the factors influencing the atmospheric BC concentration, and helps to raise public attention to the increasingly serious climate change and public health problems caused by BC., Competing Interests: The authors declare there are no competing interests., (©2024 Xie et al.)

The concentration of PM 2.5 (particulate matter with a diameter < 2.5 µm) on Sumatra Island has increased, mainly because of forest and peatland fires, transportation, and industry. Biomass burning releases partially burned carbon into the atmosphere, resulting in a smoky haze containing PM 2.5 . Air quality has deteriorated quickly, and PM 2.5 has become a major health hazard in Indonesia. Studies on long-term exposure to PM 2.5 have indicated its associations with both morbidity and mortality. Here, we measured long-term (2000-2014) exposure to PM 2.5 on the basis of satellite-derived aerosol optical depth measurements (1 × 1 km 2 ) used to predict ground-level PM 2.5 concentrations. Additionally, population data on Sumatra Island residents from the fourth wave of the Indonesian Family Life Survey (IFLS) were obtained. We investigated the association between long-term PM 2.5 exposure and mortality with a retrospective cohort study design. A total of 2409 subjects aged ≥ 40 years participated in the IFLS-3 beginning in November 2000, and we examined mortality outcomes until the IFLS-5 in September 2014. We used Cox regression models to calculate hazard ratios (HRs) of mortality associated with PM 2.5 exposure. According to the adjusted model, the mortality HRs per 10 µg/m 3 increase in PM 2.5 concentration were 1.10 (95% CI 1.03, 1.17) for all natural causes, 1.17 (95% CI 1.05, 1.25) for cardiovascular causes, and 1.19 (95% CI 1.04, 1.36) for respiratory causes. Long-term exposure to PM 2.5 was associated with all-natural, cardiovascular, and respiratory mortality on Sumatra Island, where PM 2.5 levels exceed the WHO and US-EPA air quality standards., (© 2024. The Author(s).)

Air pollution is a global issue that demands urgent attention due to its detrimental effects on human health and the environment. Land Use and Land Cover (LULC) change is an essential factor that significantly impacts ambient air quality through alterations in emission sources, vegetation cover, natural processes, and urban design. This study investigates the spatio-temporal variation of key air pollutants resulting from urban LULC changes in the Delhi region. Findings reveal a notable increase in pollutant concentrations, particularly particulate matter, in 2019 (PM 10 : 318.65 ± 45.80 µg/m 3 ) and 2023 (PM 10 : 383.70 ± 61.49 µg/m 3 ), compared to 2008 (PM 10 : 246.76 ± 30.66). LULC change analysis demonstrates a rise in built-up areas 24.59%(2008 to 2019), 33.62% (2008 to 2023) and a decline in vegetation cover 27.49% (2008 to 2019),32.37% (2008 to 2023). Correlation analysis indicates a positive correlation between PM 10 and urban indices (+ 0.63) and a negative correlation between PM 10 and vegetation indices (- 0.61), highlighting the impact of LULC on air quality deterioration. Subsequently, a fuzzy inference system model integrates LULC information to develop an air quality index (AQI). Incorporating LULC changes in AQI assessment offers a realistic approach to address the complexity arising from combined air pollutant effects, surpassing conventional AQI calculation methods. The findings underscore the significance of understanding the impact of Land Use and Land Cover (LULC) change on ambient air quality in formulating effective air quality management programs and policies. Integrating this knowledge into policymaking is crucial for the successful abatement of air pollution in urbanized areas., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Importance: The associations of exposure to fine particulate matter (PM2.5) and its constituents with spontaneous preterm birth (sPTB) remain understudied. Identifying subpopulations at increased risk characterized by socioeconomic status and other environmental factors is critical for targeted interventions., Objective: To examine associations of PM2.5 and its constituents with sPTB., Design, Setting, and Participants: This population-based retrospective cohort study was conducted from 2008 to 2018 within a large integrated health care system, Kaiser Permanente Southern California. Singleton live births with recorded residential information of pregnant individuals during pregnancy were included. Data were analyzed from December 2023 to March 2024., Exposures: Daily total PM2.5 concentrations and monthly data on 5 PM2.5 constituents (sulfate, nitrate, ammonium, organic matter, and black carbon) in California were assessed, and mean exposures to these pollutants during pregnancy and by trimester were calculated. Exposures to total green space, trees, low-lying vegetation, and grass were estimated using street view images. Wildfire-related exposure was measured by the mean concentration of wildfire-specific PM2.5 during pregnancy. Additionally, the mean exposure to daily maximum temperature during pregnancy was calculated., Main Outcomes and Measures: The primary outcome was sPTB identified through a natural language processing algorithm. Discrete-time survival models were used to estimate associations of total PM2.5 concentration and its 5 constituents with sPTB. Interaction terms were used to examine the effect modification by race and ethnicity, educational attainment, household income, and exposures to green space, wildfire smoke, and temperature., Results: Among 409 037 births (mean [SD] age of mothers at delivery, 30.3 [5.8] years), there were positive associations of PM2.5, black carbon, nitrate, and sulfate with sPTB. Adjusted odds ratios (aORs) per IQR increase were 1.15 (95% CI, 1.12-1.18; P < .001) for PM2.5 (IQR, 2.76 μg/m3), 1.15 (95% CI, 1.11-1.20; P < .001) for black carbon (IQR, 1.05 μg/m3), 1.09 (95% CI, 1.06-1.13; P < .001) for nitrate (IQR, 0.93 μg/m3), and 1.06 (95% CI, 1.03-1.09; P < .001) for sulfate (IQR, 0.40 μg/m3) over the entire pregnancy. The second trimester was the most susceptible window; for example, aORs for total PM2.5 concentration were 1.07 (95% CI, 1.05-1.09; P < .001) in the first, 1.10 (95% CI, 1.08-1.12; P < .001) in the second, and 1.09 (95% CI, 1.07-1.11; P < .001) in the third trimester. Significantly higher aORs were observed among individuals with lower educational attainment (eg, less than college: aOR, 1.16; 95% CI, 1.12-1.21 vs college [≥4 years]: aOR, 1.10; 95% CI, 1.06-1.14; P = .03) or income (<50th percentile: aOR, 1.17; 95% CI, 1.14-1.21 vs ≥50th percentile: aOR, 1.12; 95% CI, 1.09-1.16; P = .02) or who were exposed to limited green space (<50th percentile: aOR, 1.19; 95% CI, 1.15-1.23 vs ≥50th percentile: aOR, 1.12; 95% CI, 1.09-1.15; P = .003), more wildfire smoke (≥50th percentile: aOR, 1.19; 95% CI, 1.16-1.23 vs <50th percentile: aOR, 1.13; 95% CI, 1.09-1.16; P = .009), or extreme heat (aOR, 1.51; 95% CI, 1.42-1.59 vs mild temperature: aOR, 1.11; 95% CI, 1.09-1.14; P < .001)., Conclusions and Relevance: In this study, exposures to PM2.5 and specific PM2.5 constituents during pregnancy were associated with increased odds of sPTB. Socioeconomic status and other environmental exposures modified this association.

Global environmental pollution and rapid climate change have become a serious matter of concern. Remarkable spatial and seasonal variations have been observed due to rapid industrialization, urbanization, different festive occasions, etc. Among all the existing pollutants, the fine airborne particles PM 2.5 (with aerodynamic equivalent diameter ≤ 2.5 μ m ) and PM 10 (with aerodynamic equivalent diameter ≤ 10 μ m ) are associated with chronic diseases. This leads to carry out the study regarding the varying relationship between PM 2.5 and other associated factors so that its concentration level might be under control. Existing literature has explored the geographical association between the pollutants and a few other important factors. To address this problem, the present study aims to explore the wide spatio-temporal relationships between the particulate matter ( PM 2.5 ) with the other associated factors (e.g., socio-demographic, meteorological factors, and air pollutants). For this analysis, the geographically weighted regression (GWR) model with different kernels (viz. Gaussian and Bisquare kernels) and the ordinary least squares (OLS) model have been carried out to analyze the same from the perspective of the four major seasons (i.e., autumn, winter, summer, and monsoon) in different districts of India. It may be inferred from the results that the local model (i.e., GWR model with Bisquare kernel) captures the spatial heterogeneity in a better way and their performances have been compared in terms of R 2 values ( > 0.99 in all cases) and corrected Akaike information criterion ( AIC c ) (maximum value - 618.69 and minimum value - 896.88 ). It has been revealed that there is a strong negative impact between forest coverage and PM pollution in northern India during the major seasons. The same has been found in Delhi, Haryana, and a few districts of Rajasthan during the 1-year cycle (October 2022-September 2023). It has also been found that PM concentration levels become high over the specified period with the temperature drop in Delhi, Uttar Pradesh, etc. Moreover, a strong positive association is visible in PM pollution level with the total population., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background Climate change adversely affects human health, resulting in higher demand for health care services. However, the impact of climate-related environmental exposures on medical imaging utilization is currently unknown. Purpose To determine associations of short-term exposures to ambient heat and particulate air pollution with utilization of emergency department medical imaging. Materials and Methods In this retrospective time-stratified case-crossover study, daily imaging utilization counts from four emergency departments were linked to local daily environmental data-including fine particulate matter with 2.5-µm or smaller aerodynamic diameter (PM 2.5 ) and ambient temperature-over 10 years (January 2013 to December 2022). Conditional Poisson regression models were used to evaluate the associations between daily imaging utilization and environmental exposures on the same day and each of the 7 days preceding imaging, lag days 0-7, controlling for day of the week, month, and year. Moving averages of mean daily PM 2.5 and temperature were calculated to account for lagged exposure effects. Imaging counts were also stratified by modality (CT, radiography, US, and MRI). Results In an analysis of 1 666 420 emergency department imaging studies, a rise of 10 °C in the 2-day moving average of mean daily temperature and a rise of 10 μg/m 3 in the 3-day moving average of mean daily PM 2.5 were associated with overall imaging utilization increases of 5.1% (incidence rate ratio [IRR], 1.051; 95% CI: 1.045, 1.056) and 4.0% (IRR, 1.040; 95% CI: 1.035, 1.046), respectively. Heat exposure days (mean temperature >20 °C) and air pollution exposure days (mean PM 2.5 >12 μg/m 3 ) were associated with same-day excess absolute risk of 5.5 and 6.4 imaging studies per 1 million people at risk per day, respectively. Heat exposure days and air pollution exposure days were associated with increased utilization of radiography (excess relative risk, 2.7% [ P < .001] and 2.1% [ P < .001], respectively) and CT (excess relative risk, 2.0% [ P = .001] and 2.7% [ P < .001]) but not US ( P = .14 and P = .14) or MRI ( P = .70 and P = .65). Conclusion Short-term exposures to ambient heat and particulate air pollution were associated with increased utilization of radiography and CT but not US or MRI. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Vosshenrich in this issue.

Limited evidence was available on ambient air pollution and pediatric atopic dermatitis (AD). The study aimed to evaluate the associations between short-term exposure to air pollutants and outpatient visits for pediatric AD. From 2016-2018, we collected data on six criteria air pollutants (PM 2.5 , PM 10 , NO 2 , SO 2 , CO and O 3 ) and daily outpatient visits for pediatric AD in 66 hospitals, covering all districts in Shanghai, China. The over-dispersed Poisson generalized additive model (GAM) was applied to fit the associations of criteria air pollutants with hospital visits. Two-pollutant models were fitted and stratified analyses by sex, age and season were conducted. We identified 477,833 outpatient visits for pediatric AD. Each interquartile range (IQR) increase in PM 2.5 (IQR: 30.9 μg/m 3 ), PM 10 (8.9 μg/m 3 ), NO 2 (25.5 μg/m 3 ), SO 2 (5.8 μg/m 3 ) and CO (0.283 mg/m 3 ) on the concurrent day was significantly associated with increments of 2.08 % (95 % CI: 0.53 %, 3.65 %), 2.53 % (95 % CI: 0.87 %, 4.22 %), 8.14 % (95 % CI: 6.24 %, 10.08 %), 5.67 % (95 % CI: 3.58 %, 7.80 %), and 2.27 % (95 % CI: 0.70 %, 3.87 %) in pediatric AD outpatient visits, respectively. The effects of NO 2 remained robust after adjustment for other air pollutants. The exposure-response curves for PM 2.5 and PM 10 were steeper for moderate-lower concentrations, with a flatten curves at high concentration; nearly linear relationships were found for NO 2 . Higher associations of NO 2 exposure on AD were detected in children under 6 years old (p=0.01); and we observed larger effect of air pollutants in cool seasons (p<0.001 for PM 2.5 , PM 10 , NO 2 and CO; p=0.043 for SO 2 ). This study indicated that short-term exposure to air pollution could increase risk of outpatient visits for pediatric AD., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

In this study, Vanadium (V) concentrations were analyzed in five tree species (Pinus pinaster, Cupressus arizonica, Picea orientalis, Cedrus atlantica, and Pseudotsuga menziesii) from Duzce city (Turkey), a highly polluted area. The research compared V levels across species, organs, directions, and age groups over the past 40 years. Trees, which absorb nutrients from soil and air, were used as biomonitors. Cupressus arizonica and Cedrus atlantica emerged as effective for V mitigation due to high wood accumulation. Significant inter- and intra-species variations in V concentrations were noted, underscoring their potential as V indicators. Annual rings from older trees offer a long-term monitoring method. This study highlights the importance of species-specific selection for monitoring heavy metals and the role of wood in preventing reintroduction of metals into ecosystems. It enhances our understanding of V dynamics and the environmental monitoring potential of these species., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)