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Objectives: This paper presents the study design of the Berlin-Brandenburg Air study (BEAR-study). We measure air quality in Berlin and Brandenburg before and after the relocation of aircraft (AC) traffic from Tegel (TXL) airport to the new Berlin-Brandenburg airport (BER) and investigate the association of AC-related ultrafine particles (UFP) with health outcomes in schoolchildren. Methods: The BEAR-study is a natural experiment examining schoolchildren attending schools near TXL and BER airports, and in control areas (CA) away from both airports and associated air corridors. Each child undergoes repeated school-based health-examinations. Total particle number concentration (PNC) and meteorological parameters are continuously monitored. Submicrometer particle number size distribution, equivalent black carbon, and gas-phase pollutants are collected from long-term air quality monitoring stations. Daily source-specific UFP concentrations are modeled. We will analyze short-term effects of UFP on respiratory, cardiovascular, and neurocognitive outcomes, as well as medium and long-term effects on lung growth and cognitive development. Results: We examined 1,070 children (as of 30 November 2022) from 16 schools in Berlin and Brandenburg. Conclusion: The BEAR study increases the understanding of how AC-related UFP affect children's health., Competing Interests: Author SaL was employed by the company Cardinal Health Real-World Evidence and Insights. The remaining authors declare that they do not have any conflicts of interest., (Copyright © 2023 Soppa, Lucht, Ogurtsova, Buschka, López-Vicente, Guxens, Weinhold, Winkler, Wiedensohler, Held, Lüchtrath, Cyrys, Kecorius, Gastmeier, Wiese-Posselt and Hoffmann.)

Buildings are often located near ambient air pollution sources such as wildfire or heavy traffic areas. While windows in buildings are intermittently open for free cooling or natural ventilation, increased leakage area can lead to elevated human exposure to air pollutants of outdoor origin. The objective of this study is to investigate the effectiveness of paper filter windows in reducing exposure to outdoor air pollution and improving indoor air quality. The physical properties of paper windows as filtration media were experimentally determined, based on which multi-zone indoor air quality and ventilation analysis (CONTAM) simulations were performed for a full-scale building. The results show that the outdoor-indoor air exchange rate of a building can increase about 100% when conventional windows are replaced with paper filter windows. Even with the increased air exchange rate, the infiltration of outdoor particles into the building was reduced about 57-77% for the particle size range of 7-300 nm. These findings imply that paper windows have potential benefits for controlling both outdoor originated pollutants and indoor-generated pollutants with minimal energy inputs, especially in cities and communities impacted by urban air pollution and wildfires., 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 © 2023 Elsevier Ltd. All rights reserved.)

The pulp and paper industry is a major contributor to water and air pollution globally. Pulp and paper processing is an intensive energy consuming process that produces multiple contaminants that pollute water, air, and affect ecological and human health. In Canada, the National Pollutant Release Inventory (NPRI) is used to assess the release of air pollutants into the atmosphere from industrial facilities (including pulp and paper mills) and provides a repository of annual emissions reported by individual facilities. This study compared annual air emissions of carbon monoxide, nitrogen oxides, total particulate matter (TPM), PM2.5, PM10, sulphur dioxide, and volatile organic compounds from nine different pulp and/or paper mills in Atlantic Canada from three provinces (Nova Scotia, New Brunswick, and Newfoundland and Labrador) between 2002 and 2019. Results revealed that annual releases were several orders of magnitude higher than federal reporting thresholds suggested by Environment and Climate Change Canada. Pulp mills emit higher pollutant loads than those producing paper. The highest exceedance of a reporting threshold was for particulate matter (PM2.5) at Northern Pulp in Nova Scotia. The emissions of PM2.5 were on average (over a 17-year period) about 100,000% above the reporting threshold of 0.3 tonnes per year.

Objectives: This paper presents the study design of the Berlin-Brandenburg Air study (BEAR-study). We measure air quality in Berlin and Brandenburg before and after the relocation of aircraft (AC) traffic from Tegel (TXL) airport to the new Berlin-Brandenburg airport (BER) and investigate the association of AC-related ultrafine particles (UFP) with health outcomes in schoolchildren.Methods: The BEAR-study is a natural experiment examining schoolchildren attending schools near TXL and BER airports, and in control areas (CA) away from both airports and associated air corridors. Each child undergoes repeated school-based health-examinations. Total particle number concentration (PNC) and meteorological parameters are continuously monitored. Submicrometer particle number size distribution, equivalent black carbon, and gas-phase pollutants are collected from long-term air quality monitoring stations. Daily source-specific UFP concentrations are modeled. We will analyze short-term effects of UFP on respiratory, cardiovascular, and neurocognitive outcomes, as well as medium and long-term effects on lung growth and cognitive development.Results: We examined 1,070 children (as of 30 November 2022) from 16 schools in Berlin and Brandenburg.Conclusion: The BEAR study increases the understanding of how AC-related UFP affect children’s health.

Pulp and paper mills are known large emitters of air pollution creating potential environmental challenges and human health impacts. This study assesses air pollution and associated impacts from connected pulp and paper facilities which operate in Edmundston, New Brunswick, Canada, and Madawaska, Maine, United States (US). Despite operating within two regulatory jurisdictions on both sides of the international border (in Canada and the US), the mills have exceeded their approvals to operate and national air quality regulations multiple times between 2010 and 2017. Previous studies have linked many types of cancer as well as respiratory and cardiovascular diseases to air pollution emitted by pulp and paper mills, making this an important regional issue in Canada and the US. While potential environmental and community impacts are likely similar on both sides of the international border, emissions from each side are not reported on the other, and few studies have been conducted on either side. Historic and ongoing lack of local epidemiological health and environmental impact studies and recommendations for improvements to mill operations to improve environmental and human health are presented.

This paper looks at the relationship between enterprise size and pollution abatement costs using firm-level datasets in China, where size-dependent distortions may exist in the distribution of enterprises of different sizes. We find that the pollution abatement costs decrease with enterprise size. Compared to the smallest size, the magnitudes of marginal decreases in pollution abatement costs per 1000 CNY of industrial output in the largest size are 3.29 CNY, 0.54 CNY and 2.60 CNY for gas, SO 2 and dust, respectively. Moreover, we further explore the potential mechanisms for the decrease of pollution abatement costs from three perspectives: environmental protection-related investment, regulation intensity, and technological capability. Our findings suggest that the decrease in the intensity of environmental protection-related investment for large enterprises may be directly reduce the intensity of pollution abatement costs. Although large enterprises have low intensity of pollution abatement costs, this does not mean that they are subject to weaker environmental regulation. More importantly, we find that large enterprises may have more adoption of abatement technologies. For SO 2 and dust, the pollutant removal efficiency of the largest enterprises is 4.92% and 1.82% higher than that of the smallest enterprises, respectively. Our results are robust to different specifications. These findings help us enact appropriate policies for enterprises based on their size, thereby improving the efficacy of pollution abatement and achieving the optimal level of pollution abatement for the entire society., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The health effects of air pollution remain a public concern worldwide. Using data from the Global Burden of Disease 2019 report, we statistically analyzed total mortality, disability-adjusted life years (DALY), and years of life lost (YLL) attributable to air pollution in eight East African countries between 1990 and 2019. We acquired ambient ozone (O 3 ), PM 2.5 concentrations and household air pollution (HAP) from the solid fuel from the State of Global Air report. The multilinear regression model was used to evaluate the predictability of YLLs by the air pollutants. We estimated the ratio rate for each health burden attributable to air pollution to compare the country's efforts in the reduction of air pollution health burden. This study found that the total number of deaths attributable to air pollution decreased by 14.26% for 30 years. The drop came from the reduction of 43.09% in mortality related to Lower Respiratory tract Infection (LRI). However, only five out of eight countries managed to decrease the total number of deaths attributable to air pollution with the highest decrease observed in Ethiopia (40.90%) and the highest increase in Somalia (67.49%). The linear regression model showed that HAP is the pollutant of the most concern in the region, with a 1% increase in HAP resulting in a 31.06% increase in regional YLL (R 2  = 0.93; p < 0.05). With the increasing ground-level ozone, accompanied by the lack of adequate measures to reduce particulate pollutants, the health burdens attributable to air pollution are still a threat in the region., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

In this reflective paper we outline and discuss our art-based Patient and Public Involvement (PPI) approach. This exercise held two broad objectives. Firstly, to assist policy makers in understanding the types of interventions communities will find acceptable to address the problem of poor air quality, and secondly, to ascertain community views about our research plans to explore the impact of the planned interventions on neighbourhoods. We reflect on both our approach and the emergent conversations from the PPI activity. Attendees contributed to the process and stressed the importance of not burdening poor neighbourhoods with costly charges as that would ameliorate one health problem but generate others as a consequence of additional financial burden. Equally, they stressed the need to conduct research on matters which they could connect with such as the impact of clean air plans on young children and how information about air pollution is disseminated in their neighbourhoods as and when research findings emerge. This paper offers a conceptual analysis of the art-based PPI method and uniquely draws a connection to the philosophical traditions of Ludwig Wittgenstein. Specifically, we demonstrate how art is conducive to creating a dialogue which is specifically helpful for PPI purposes for both researchers and implementers, and conversely, why traditional conversational approaches may have fallen short of the adequacy mark in this regard.

This study identifies factors influencing spatial and temporal variations in magnetic susceptibility and heavy metal content in soils and airborne particulate matter within the Kyiv megapolis, Ukraine, and highlights how source apportionment differs in the long and short run. Topsoil magnetic susceptibility anomalies of > 70 × 10 -8 m 3 kg -1 are observed around old factories. The tree bark magnetic susceptibility map provides a record of industry general low emissions for the last 2-3 decades. The patterns of both spatial distributions confirm that factory emissions dominate the composition of particulate falling on the ground in urban area, with exclusion of streets with heavy traffic. Enhanced concentrations of Cu, Ni, and Zn have been found in urban soils, showing a positive correlation with magnetic susceptibility. Re-suspended road dust dominates temporal variation of particulate matter magnetic susceptibility collected on air filters. The air at busy streets is cleaner in winter, when the street dust gets immobilized by snow cover or freezing. Industries in Kyiv pose no significant effect on air quality; the concentrations of Cr, Ni, Cu, Zn, Cd, and Pb are at normal urban level with the exception of the near vicinity to factories. Air in streets with heavy traffic is enriched with Fe and Mn. Principal component analysis reveals different pattern of air pollution for the busy streets and yard areas. Yards are less affected by road dust; thus, contribution of industrial emissions can be distinguished. The results provide context for further quantification of any alterations in ecological state of Kyiv megapolis that may have arisen from socio-economic shocks and direct threats connected to the current war., (© 2024. The Author(s).)

Pakistan is among the South Asian countries mostly vulnerable to the negative health impacts of air pollution. In this context, the study aimed to analyze the spatiotemporal patterns of chronic obstructive pulmonary disease (COPD) incidence and its relationship with air pollutants including aerosol absorbing index (AAI), carbon monoxide, sulfur dioxide (SO2), and nitrogen dioxide. Spatial scan statistics were employed to identify temporal, spatial, and spatiotemporal clusters of COPD. Generalized linear regression (GLR) and random forest (RF) models were utilized to evaluate the linear and non-linear relationships between COPD and air pollutants for the years 2019 and 2020. The findings revealed three spatial clusters of COPD in the eastern and central regions, with a high-risk spatiotemporal cluster in the east. The GLR identified a weak linear relationship between the COPD and air pollutants with R 2 = 0.1 and weak autocorrelation with Moran's index = -0.09. The spatial outcome of RF model provided more accurate COPD predictions with improved R 2 of 0.8 and 0.9 in the respective years and a very low Moran's I = -0.02 showing a random residual distribution. The RF findings also suggested AAI and SO2 to be the most contributing predictors for the year 2019 and 2020. Hence, the strong association of COPD clusters with some air pollutants highlight the urgency of comprehensive measures to combat air pollution in the region to avoid future health risks., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Extreme PM 2.5 pollution has become a significant environmental problem in China in recent years, which is hazardous to human health and daily life. Noticing the importance of investigating the causes of extreme PM 2.5 pollution, this paper classifies cities across China into eight categories (four groups plus two scenarios) based on the generalized extreme value (GEV) distribution using hourly station-level PM 2.5 concentration data, and a series of multi-choice models are employed to assess the probabilities that cities fall into different categories. Various factors such as precursor pollutants and socio-economic factors are considered after controlling for meteorological conditions in each model. It turns out that SO 2 concentration, NO 2 concentration, and population density are the top three factors contributing most to the log ratios. Moreover, in both left- and right-skewed cases, the influence of a one-unit increase of SO 2 concentration on the relative probability of cities falling into different groups shows an increasing trend, while those of NO 2 concentration show a decreasing trend. At the same time, the higher the extreme pollution level, the bigger the effect of SO 2 and NO 2 concentrations on the probability of cities falling into normalized scenarios. The multivariate logit model is used for prediction and policy simulations. In summary, by analyzing the influences of various factors and the heterogeneity of their influence patterns, this paper provides valuable insights in formulating effective emission reduction policies., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

In China, despite the fact that the atmospheric environment quality has continued to improve in recent years, the PM 2.5 pollution still had not been controlled fundamentally and its driving mechanism was complex which remained to be explored. Based on the 1-km ground-level PM 2.5 datasets of China from 2000 to 2020, this study combined spatial autocorrelation, trend analysis, geographical detector, and multi-scale geographically weighted regression (MGWR) model to explore the spatial-temporal evolution of PM 2.5 in Shanxi Province and revealed its complex driving mechanism behind this process. The results reflected that (1) there was a pronounced spatial clustering of PM 2.5 concentration within Shanxi Province, with PM 2.5 concentrations decreasing from southwest to northeast. From 2000 to 2020, the levels of PM 2.5 pollution demonstrated a decline over time, with its concentrations decreasing by 9.15 µg/m 3 overall. The Hurst exponent indicated a projected decrease in PM 2.5 concentrations in the central and northern areas of Shanxi Province, contrasting with an anticipated increase in other regions. (2) The geographical detector indicated that all drivers had significant influences on PM 2.5 concentrations, with meteorological factors exerting the greatest effects then followed by human activity and vegetation cover showing the least effects. (3) Both gross domestic product and population density exhibited positive correlations with PM 2.5 concentration, while vegetation fractional cover, wind speed, precipitation, and elevation exerted negative influences on PM 2.5 concentration all over the space. This study enriched the research content and ideas on the driving mechanism of PM 2.5 and provided a reference for similar studies., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Adıyaman, a city recently affected by an earthquake, is facing significant air pollution challenges due to both anthropogenic activities and natural events. The sources of air pollution have been investigated using meteorological variables. Elevated southerly winds, especially prominent in spring and autumn, significantly contribute to dust transport, leading to a decline in local air quality as detected by the HYSPLIT model. Furthermore, using Suomi-NPP Thermal Anomaly satellite product, it is detected and analyzed for crop burning activities. Agricultural practices, including stubble burning, contribute to the exacerbation of PM 10 pollution during the summer months, particularly when coupled with winds from all directions except the north. In fall and winter months, heating is identified as the primary cause of pollution. The city center located north of the station is the dominant source of pollution throughout all seasons. The study established the connection between air pollutants and meteorological variables. Furthermore, the Spearman correlation coefficients reveal associations between PM 10 and SO 2 , indicating moderate positive correlations under pressure conditions (r = 0.35, 0.52). Conversely, a negative correlation is observed with windspeed (r = -0.35, -0.50), and temperature also exhibits a negative correlation (r = -0.39, -0.54). During atmospheric conditions with high pressure, PM 10 and SO 2 concentrations are respectively 41.2% and 117.2% higher. Furthermore, pollutant concentration levels are 29.2% and 53.3% higher on days with low winds. Last, practical strategies for mitigating air pollution have been thoroughly discussed and proposed. It is imperative that decision-makers engaged in city planning and renovation give careful consideration to the profound impact of air pollution on both public health and the environment, particularly in the aftermath of a recent major earthquake., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

In this study, the levels of sulfur dioxide (SO 2 ) and nitrogen dioxide (NO 2 ) were measured indoors and outdoors using passive samplers in Tymar village (20 homes), an industrial area, and Haji Wsu (15 homes), a non-industrial region, in the summer and the winter seasons. In comparison to Haji Wsu village, the results showed that Tymar village had higher and more significant mean SO 2 and NO 2 concentrations indoors and outdoors throughout both the summer and winter seasons. The mean outdoor concentration of SO 2 was the highest in summer, while the mean indoor NO 2 concentration was the highest in winter in both areas. The ratio of NO 2 indoors to outdoors was larger than one throughout the winter at both sites. Additionally, the performance of machine learning (ML) approaches: multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) were compared in predicting indoor SO 2 concentrations in both the industrial and non-industrial areas. Factor analysis (FA) was conducted on different indoor and outdoor meteorological and air quality parameters, and the resulting factors were employed as inputs to train the models. Cross-validation was applied to ensure reliable and robust model evaluation. RF showed the best predictive ability in the prediction of indoor SO 2 for the training set (RMSE = 2.108, MAE = 1.780, and R 2 = 0.956) and for the unseen test set (RMSE = 4.469, MAE = 3.728, and R 2 = 0.779) values compared to other studied models. As a result, it was observed that the RF model could successfully approach the nonlinear relationship between indoor SO 2 and input parameters and provide valuable insights to reduce exposure to this harmful pollutant., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Tropospheric ozone is an air pollutant at the ground level and a greenhouse gas which significantly contributes to the global warming. Strong anthropogenic emissions in and around urban environments enhance surface ozone pollution impacting the human health and vegetation adversely. However, observations are often scarce and the factors driving ozone variability remain uncertain in the developing regions of the world. In this regard, here, we conducted machine learning (ML) simulations of ozone variability and comprehensively examined the governing factors over a major urban environment (Ahmedabad) in western India. Ozone precursors (NO 2 , NO, CO, C 5 H 8 and CH 2 O) from the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis and meteorological parameters from the ERA5 (European Centre for Medium-Range Weather Forecast's (ECMWF) fifth-generation reanalysis) were included as features in the ML models. Automated ML (AutoML) fitted the deep learning model optimally and simulated the daily ozone with root mean square error (RMSE) of ~2 ppbv reproducing 84-88% of variability. The model performance achieved here is comparable to widely used ML models (RF-Random Forest and XGBoost-eXtreme Gradient Boosting). Explainability of the models is discussed through different schemes of feature importance, including SAGE (Shapley Additive Global importancE) and permutation importance. The leading features are found to be different from different feature importance schemes. We show that urban ozone could be simulated well (RMSE = 2.5 ppbv and R 2  = 0.78) by considering first four leading features, from different schemes, which are consistent with ozone photochemistry. Our study underscores the need to conduct science-informed analysis of feature importance from multiple schemes to infer the roles of input variables in ozone variability. AutoML-based studies, exploiting potentials of long-term observations, can strongly complement the conventional chemistry-transport modelling and can also help in accurate simulation and forecast of urban ozone., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The goal of the current study is to evaluate the heavy metal rainfall contamination in the vicinity brought on by the Erbil Steel Factory in Iraq during the study period. The study's findings revealed the concentration of all studied heavy metals in the precipitation near and around the factory is significantly higher than that of the rural area of Barzan village which is used as a control site. The average concentration of the metals is in descending order manganese (Mn) > lead (Pb) > iron (Fe) > arsenic (As) > cobalt (Co) > selenium (Se) > mercury (Hg) > and cadmium (Cd) for the polluted site. The geo-accumulation index (I-geo) of the heavy metal Mn in the rainfall around the steel factory site is 6.28 > 5 which indicates extreme contamination. While the Igeo values of Cd, As, and Fe are 4.87, 4.54, and 4.04 > 4 that indicate heavy to extreme contamination, for Pb, 3.80 > 3 indicates moderate to heavy contamination, Cd 1.68 > 1 indicates moderate contamination, Hg 0.46 > 0 indicates uncontaminated to moderate contamination, and Se - 0.36 < 0 indicates uncontaminated. The pollution load index (PLI) of the rainwater around the steel factory site is 13.46 > 1, demonstrating that the area is highly metal-contaminated., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Aerosolized microorganisms have become an important factor in assessing air quality. To determine the characteristics of bacterial bioaerosols in air and rainwater, as well as calculate the recovery rate of bacteria after rains in Ho Chi Minh City, our study was performed using the SKC Biostage sampler for airborne bacteria and Plate Count Agar (PCA) medium for bacterial concentration. Subsequently, the study determined the bacterial community composition at the phylum and order levels using the 16S rRNA (16S metabarcoding) method. Before the rain, bacterial concentrations in the air ranged from 263.39 ± 21.00 to 277.39 ± 78.99 CFU/m 3 , and in rainwater 264.89 ± 51.17 to 285.72 ± 28.00 CFU/m 3 . Following rains, the bacterial concentrations decreased to their lowest levels within the first 1-2 h and gradually increased thereafter, reaching their peak after 9 h for heavy rain and after 12 h for light and moderate rains. The bacterial bioaerosols recovery rate was determined to be 100% for light and moderate rains and 94.6% for heavy rain. The change in bacterial concentration after rainfall showed a positive correlation with temperature (r = 0.85) and CO 2 concentration (r = 0.70) and a negative correlation with relative humidity (r =  - 0.79). Bacterial composition analysis revealed that the Actinobacteria, Firmicutes, and Proteobacteria phyla were dominant and characteristic of the humid tropical climate in Vietnam. Notably, Firmicutes were the most prevalent phylum both before and after rains. The increased prevalence of certain bacterial orders, particularly Staphylococcus, could contribute to the spread of pathogens, particularly foodborne pathogens. In addition to rain, relative humidity contributed to reducing bacterial bioaerosols concentration and their recovery rate after the rain., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

In recent years, several studies have reported the recycling of by-products generated by the paper industry and their application to the construction industry. A majority of the existing studies used waste paper sludge ash, and considerable energy is consumed in such incineration processes. This may further contribute to air pollution. In this study, we used waste newspaper (WNP), which underwent a simple crushing process without a separate high-temperature treatment process, and we integrated it in cement mortar. We prepared mortars containing 0%, 0.2%, 0.4%, 0.6%, 0.8%, and 1.0% ground WNP as a cement substitute. Subsequently, the fluidity, compressive strength, tensile strength, carbonation depth, drying shrinkage, and microstructure of the mortars were compared and analyzed. The 28-day compressive strength of the mortar samples with WNP was approximately 3.2–16.1% higher than that of the control sample. The 28-day accelerated carbonation depth of the samples with WNP was approximately 1.03–1.61 mm. Furthermore, their carbonation resistance was approximately 5.2–39.4% higher than that of the control sample. Compressive strength, tensile strength, and carbonation resistance were improved by appropriately using ground WNP as a cement substitute in cement mortar. In this study, the appropriate amount of WNP according to the mechanical properties of cement mortar was found to be 0.4–0.8%, and considering the durability characteristics, the value 0.6 was the most ideal. [ABSTRACT FROM AUTHOR]

This paper investigates the air quality in 107 Italian provinces in the period 2014-2019 and the association between exposure to nine outdoor air pollutants and the COVID-19 spread and related mortality in the same areas. The methods used were negative binomial (NB) regression, ordinary least squares (OLS) model, and spatial autoregressive (SAR) model. The results showed that (i) common air pollutants-nitrogen dioxide (NO 2 ), ozone (O 3 ), and particulate matter (PM 2.5 and PM 10 )-were highly and positively correlated with large firms, energy and gas consumption, public transports, and livestock sector; (ii) long-term exposure to NO 2 , PM 2.5 , PM 10 , benzene, benzo[a]pyrene (BaP), and cadmium (Cd) was positively and significantly correlated with the spread of COVID-19; and (iii) long-term exposure to NO 2, O 3 , PM 2.5 , PM 10 , and arsenic (As) was positively and significantly correlated with COVID-19 related mortality. Specifically, particulate matter and Cd showed the most adverse effect on COVID-19 prevalence; while particulate matter and As showed the largest dangerous impact on excess mortality rate. The results were confirmed even after controlling for eighteen covariates and spatial effects. This outcome seems of interest because benzene, BaP, and heavy metals (As and Cd) have not been considered at all in recent literature. It also suggests the need for a national strategy to drive down air pollutant concentrations to cope better with potential future pandemics., (© 2022. The Author(s).)

Historically, Romania is known as a mining site of mineral substances, including gold, silver, copper, lead, zin, uranium, manganese, salt, and coal, whereby their long periods of exploitation and extraction affected human health and the environment in various ways. In Moldova Nouă southwest region of Romania, we investigated the environmental impacts of mining activities on air quality over 2021. We quantified PM 10 emission rates through in situ monitoring, dispersion modelling, and horizontal and vertical fluxes. Statistical metrics, including the fraction within factor 2 (FAC2), mean bias (MB), mean gross error (MGE), normalized mean bias (NMB), normalized mean gross error (NMGE), coefficient of efficiency (COE), index of agreements (IOAs), and Taylor diagram signifying standards deviation (SD), root mean squared error (RMSE), and correlation coefficient (R), were used to evaluate the reliability of modelling results against observation. Results conclude that PM 10 dispersion agrees with MB, MGE, NMB, NMGE, COE, IOA, and Taylor diagram and moderately with FAC2 metrics. PM 10 hotspot was investigated in the vicinity of the tailings ponds of 115.5 µg m -3 annual mean, 563.7 µg m -3 daily mean, 63.3 µg m -2  s -1 annual horizontal flux, and 3.0 µg m -2  s -1 annual vertical flux. PM 10 dispersion was identified to expand to Moldova Nouă City and nearby country Serbia. Findings concluded that a windy air mass accumulation across the overburdened dumps and ponds causes the increase of PM 10 in the air, resulting in the region's pollution. Therefore, results recommend adopting a strategic mitigation measure for residents, policymakers, stakeholders, and urban planners., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The widespread exposure to ambient PM 2.5 poses a substantial health risk globally, with a more pronounced impact on low- to medium-income nations. This study investigates the spatiotemporal distribution of PM 2.5 in the communities hosting informal brickmaking industries in Vhembe District. Utilizing Dylos DC1700, continuous monitoring of PM 2.5 was conducted at nine stations adjacent to informal brick kilns from March 2021 to February 2022. The study determined the correction factor for PM 2.5 measurements obtained from the Dylos DC1700 when it was collocated with the GRIMM Environmental Dust Monitor 180. Additionally, the diurnal and seasonal variations across monitoring stations were assessed, and potential PM 2.5 sources were identified. The study also evaluated the compliance of ambient PM 2.5 concentrations across the stations with the South African National Ambient Air Quality Standard (NAAQS) limits. Annual PM 2.5 concentrations for the stations ranged from 22.6 to 36.2 μgm -3 . Diurnal patterns exhibited peak concentrations in the morning and evening, while seasonal variations showed higher concentrations in winter and lower concentrations in summer and spring. All monitoring stations reported the highest daily exceedance with respect to the daily NAAQS limit in the winter. Major PM 2.5 sources included domestic biomass combustion, vehicular emissions, industrial emissions, and construction sites. Well-calibrated low-cost sensors could be employed in suburb regions with scarce air quality data. Findings from the study could be used for developing mitigation strategies to reduce health risks associated with PM 2.5 exposure in the area., (© 2023. The Author(s).)

Increasing air pollution and decreasing exposure to greenness may contribute to the metabolic syndrome (MetS). We examined associations between long-term exposure to residential greenness and air pollution and MetS incidence in the Bangkok Metropolitan Region, Thailand. Data from 1369 employees (aged 52-71 years) from the Electricity Generating Authority of Thailand cohort from 2002 to 2017 were analyzed. The greenness level within 500 m of each participant's residence was measured using the satellite-derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). The kriging approach was used to generate the average concentration of each air pollutant (PM 10 , CO, SO 2 , NO 2 , and O 3 ) at the sub-district level. The average long-term exposure to air pollution and greenness for each participant was calculated over the same period of person-time. Cox proportional hazards models were used to analyze the greenness-air pollution-MetS associations. The adjusted hazard ratio of MetS was 1.42 (95% confidence interval (CI): 1.32, 1.53), 1.22 (95% CI: 1.15, 1.30), and 2.0 (95% CI: 1.82, 2.20), per interquartile range increase in PM 10 (9.5 μg/m 3 ), SO 2 (0.9 ppb), and CO (0.3 ppm), respectively. We found no clear association between NDVI or EVI and the incidence of MetS. On the contrary, the incident MetS was positively associated with NDVI and EVI for participants exposed to PM 10 at concentrations more than 50 μg/m 3 . In summary, the incidence of MetS was positively associated with long-term exposure to air pollution. In areas with high levels of air pollution, green spaces may not benefit health outcomes., (© 2023. The Author(s) under exclusive licence to International Society of Biometeorology.)

The PM 2.5 (particulate matter with a diameter of fewer than 2.5 µm) has become a global topic in environmental science. The neural network that based on the non-linear regression algorithm, e.g., deep learning, is now believed to be one of the most facile and advanced approaches in PM 2.5 concentration prediction. In this study, we proposed a PM 2.5 predictor using deep learning as infrastructure and meteorological data as input, for predicting the next hour PM 2.5 concentration in Beijing Aotizhongxin monitor point. We efficiently use the parameter's spatiotemporal correlation by concatenating the dataset with time series. The predicted PM 2.5 concentration was based on meteorology changes over a period. Therefore, the accuracy would increase with the period growing. By extracting the intrinsic features between meteorological and PM 2.5 concentration, a fast and accurate prediction was carried out. The R square score reached maximum of 0.98 and remained an average of 0.9295 in the whole test. The average bias of the model is 9 μg on the validation set and 1 μg on the training set. Moreover, the differences between the predictions and expectations can be further regarded as the estimation for the emission change. Such results can provide scientific advice to supervisory and policy workers., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Air pollution is one of the main environmental issues in densely populated urban areas like Delhi. Predictions of the PM2.5 concentration must be accurate for pollution reduction strategies and policy actions to succeed. This research article presents a novel approach for forecasting PM2.5 pollution in Delhi by combining a pre-trained CNN model with a transformer-based model called CATALYST (Convolutional and Transformer model for Air Quality Forecasting). This proposed strategy uses a mixture of the two models. To derive attributes of the PM2.5 timeline of data, a pre-existing CNN model is utilized to transform the data into visual representations, which are analyzed subsequently. The CATALYST model is trained to predict future PM2.5 pollution levels using a sliding window training approach on extracted features. The model is utilized for analyzing temporal dependencies in PM2.5 time-series data. This model incorporates the advancements in the transformer-based architecture initially designed for natural language processing applications. CATALYST combines positional encoding with the Transformer architecture to capture intricate patterns and variations resulting from diverse meteorological, geographical, and anthropogenic factors. In addition, an innovative approach is suggested for building input-output couples, intending to address the problem of missing or partial data in environmental time-series datasets while ensuring that all training data blocks are comprehensive. On a PM2.5 dataset, we analyze the proposed CATALYST model and compare its performance with other standard time-series forecasting approaches, such as ARIMA and LSTM. The outcomes of the experiments demonstrate that the suggested model works better than conventional methods and is a potential strategy for accurately forecasting PM2.5 pollution. The applicability of CATALYST to real-world scenarios can be tested by running more experiments on real-world datasets. This can help develop efficient pollution mitigation measures, impacting public health and environmental sustainability., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background: Living in greener areas is associated with slower cognitive decline and reduced dementia risk among older adults, but the evidence with neurodegenerative disease mortality is scarce. We studied the association between residential surrounding greenness and neurodegenerative disease mortality in older adults., Methods: We used data from the 2001 Belgian census linked to mortality register data during 2001-2014. We included individuals aged 60 years or older and residing in the five largest Belgian urban areas at baseline (2001). Exposure to residential surrounding greenness was assessed using the 2006 Normalized Difference Vegetation Index (NDVI) within 500-m from residence. We considered all neurodegenerative diseases and four specific outcomes: Alzheimer's disease, vascular dementia, unspecified dementia, and Parkinson's disease. We fitted Cox proportional hazard models to obtain hazard ratios (HR) and 95% confidence intervals (CI) of the associations between one interquartile range (IQR) increment in surrounding greenness and neurodegenerative disease mortality outcomes, adjusted for census-based covariates. Furthermore, we evaluated the potential role of 2010 air pollution (PM 2.5 and NO 2 ) concentrations, and we explored effect modification by sociodemographic characteristics., Results: From 1,134,502 individuals included at baseline, 6.1% died from neurodegenerative diseases during follow-up. After full adjustment, one IQR (0.22) increment of surrounding greenness was associated with a 4-5% reduction in premature mortality from all neurodegenerative diseases, Alzheimer's disease, vascular and unspecified dementia [e.g., for Alzheimer's disease mortality: HR 0.95 (95%CI: 0.93, 0.98)]. No association was found with Parkinson's disease mortality. Main associations remained for all neurodegenerative disease mortality when accounting for air pollution, but not for the majority of specific mortality outcomes. Associations were strongest in the lower educated and residents from most deprived neighbourhoods., Conclusions: Living near greener spaces may reduce the risk of neurodegenerative disease mortality among older adults, potentially independent from air pollution. Socioeconomically disadvantaged groups may experience the greatest beneficial effect., (© 2022. The Author(s).)

The aim of this study was to investigate the effect of changes in meteorological parameters and fuel composition on the emission rate of air pollutants in the vehicle fleet of the Tehran Metropolis. The results of this study can be used in management decisions to reduce the emission of air pollutants. In this paper, based on the international vehicle emission model and using mathematical equations, the effects of changing meteorological parameters and fuel composition on the emission of pollutants were modeled. The emission rates of CO, VOCs, and NOx pollutants were the most sensitive to the changes in meteorological parameters, respectively. Among all parameters studied in this research, the changes in sulfur level had the greatest effect on the emission of pollutants from the vehicle fleet of Tehran Metropolis. If the fuel was replaced with Euro 5 standard instead of Euro 3, the emission rates of CO, VOCs, NOx, PM, and SOx pollutants from the vehicle fleet of Tehran Metropolis would be reduced by 9%, 6%, 5%, 14%, and 90%, respectively. Managing and reducing the sources of production and emission of air pollution is one of the best ways to reduce the air pollution. In general, since the emission of pollutants from the fleet of Tehran Metropolis in the cold seasons of the year is greater than during hot seasons and the problem of air pollution is exacerbated by air stability, using Euro 5 fuel in cold seasons is one of the efficient ways to reduce the air pollution., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

As largely documented in the literature, the stark restrictions enforced worldwide in 2020 to curb the COVID-19 pandemic also curtailed the production of air pollutants to some extent. This study investigates the perception of the air pollution as assessed by individuals located in ten countries: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the USA. The perceptions towards air quality were evaluated by employing an online survey administered in May 2020. Participants (N = 9394) in the ten countries expressed their opinions according to a Likert-scale response. A reduction in pollutant concentration was clearly perceived, albeit to a different extent, by all populations. The survey participants located in India and Italy perceived the largest drop in the air pollution concentration; conversely, the smallest variation was perceived among Chinese and Norwegian respondents. Among all the demographic indicators considered, only gender proved to be statistically significant., (© 2021. The Author(s).)

The use of techniques based on artificial intelligence and machine learning for the simulation of many processes is becoming increasingly important in environmental sciences, with applications in the study of time series of atmospheric properties, such as pollution levels. The present work aimed to evaluate the efficiency of a model based on Artificial Neural Networks (ANN) in the simulation PM 10 from meteorological data observed between 2018 and 2019 in Guaíba, southern Brazil, thus also having an estimate of the influence of atmospheric conditions on local air pollution. For this purpose, meteorological and PM 10 data obtained from the stations Parque 35, sustained by Celulose Riograndense (CMPC), and A-801, sustained by the National Institute of Meteorology (INMET), were used. The ANN used for the simulation was of the Multilayer Perceptron type, trained by the backpropagation algorithm with cross-validation. The results obtained indicate that the simulation was satisfactory with a Nash-Sutcliffe index (NSE) of 0.64, a linear correlation coefficient (R) of 0.81, a relative error (Er) of 26% and a root mean square error (RMSE) of 7.40 µg/m 3 . Thus, even with some difficulty in estimating extreme concentrations, the model was suitable for the largest range observed, of 10 µg/m 3 to 50 µg/m 3 . For this dataset, the model proved to be an useful assessment tool and has the potential to be applied operationally to contribute to the monitoring and control of air quality levels both in the study area and in other regions of Brazil and the world., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Steady improvements in ambient air quality in the USA over the past several decades, in part a result of public policy 1,2 , have led to public health benefits 1-4 . However, recent trends in ambient concentrations of particulate matter with diameters less than 2.5 μm (PM 2.5 ), a pollutant regulated under the Clean Air Act 1 , have stagnated or begun to reverse throughout much of the USA 5 . Here we use a combination of ground- and satellite-based air pollution data from 2000 to 2022 to quantify the contribution of wildfire smoke to these PM 2.5 trends. We find that since at least 2016, wildfire smoke has influenced trends in average annual PM 2.5 concentrations in nearly three-quarters of states in the contiguous USA, eroding about 25% of previous multi-decadal progress in reducing PM 2.5 concentrations on average in those states, equivalent to 4 years of air quality progress, and more than 50% in many western states. Smoke influence on trends in the number of days with extreme PM 2.5 concentrations is detectable by 2011, but the influence can be detected primarily in western and mid-western states. Wildfire-driven increases in ambient PM 2.5 concentrations are unregulated under current air pollution law 6 and, in the absence of further interventions, we show that the contribution of wildfire to regional and national air quality trends is likely to grow as the climate continues to warm., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Ambient PM 2.5 (particles less than 2.5 μm in diameter) is monitored in many countries including Australia. Occasionally PM 2.5 instruments may report negative measurements, although in realty the ambient air can never contain negative amounts of particles. Some negative readings are caused by instrument faults or procedural errors, thus can be simply invalidated from air quality reporting. There are occasions, however, when negative readings occur due to other factors including technological or procedural limitations. Treatment of such negative data requires consideration of factors such as measurement uncertainty, instrument noise and risk for significant bias in air quality reporting. There is very limited documentation on handling negative PM 2.5 data in the literature. This paper demonstrates how a threshold is determined for controlling negative hourly PM 2.5 readings in the New South Wales (NSW) air quality data system. The investigation involved a review of thresholds used in different data systems and an assessment of instrument measurement uncertainties, zero air test data and impacts on key reporting statistics when applying different thresholds to historical datasets. The results show that a threshold of -10.0 μg/m 3 appears optimal for controlling negative PM 2.5 data in public reporting. This choice is consistent with the measurement uncertainty estimates and the zero air test data statistics calculated for the NSW Air Quality Monitoring Network, and is expected not to have significant impacts on key compliance reporting statistics such as data availability and annual average pollution levels. The analysis can be useful for air quality monitoring in other Australian jurisdictions or wider context., (© 2023. The Author(s).)