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Introduction: Air pollution is a global issue that poses a significant threat to public health. Children, due to their developing physiology, are particularly susceptible to the inhalation of environmental pollutants. Exposure can trigger immune modulation and organ damage, increasing susceptibility to respiratory diseases. Therefore, we aim to examine the association between heavy metal and particulate matter exposure with tuberculosis in children., Methods and Analysis: As a case-control study, we will include children diagnosed with pulmonary tuberculosis (n=60) and matched healthy controls (n=80) recruited from the same communities in Dhaka, Bangladesh. Exposure data for both cases and controls will be collected by a trained field team conducting home visits. They will administer an exposure questionnaire, measure child anthropometry, collect blood and household dust samples and instal 48-hour air quality monitors. The blood samples will be analysed by inductively coupled plasma mass spectrometry for serum heavy metal concentrations (lead, cadmium, arsenic, mercury and chromium), as a representative marker of exposure, and the presence of inflammatory biomarkers. Descriptive and inferential statistics, including independent samples t-tests, analysis of variance and conditional regression analysis, will be used to quantify heavy metal and particulate matter exposure status in tuberculosis cases compared with healthy controls, while accounting for potential confounders. Dust samples and air quality results will be analysed to understand household sources of heavy metal and particulate matter exposure. To test the study hypothesis, there is a positive association between exposure and tuberculosis diseases, we will also measure the accumulated effect of simultaneous exposures using Bayesian statistical modelling., Ethics and Dissemination: This study has been approved by International Centre for Diarrhoeal Disease Research, Bangladesh's Institutional Review Board (PR-22030). The study findings will be disseminated at conferences and published in peer-reviewed journals., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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 the context of pursuing carbon neutrality and balancing the use of fossil fuels with renewable energy, the transportation industry faces the challenge of accurately predicting energy demand, related emissions, and assessing the effectiveness of energy technologies and policies. This is crucial for formulating energy management plans and reducing carbon dioxide (CO 2 ) and atmospheric pollutant emissions. Currently, research on energy consumption and emission forecasting primarily relies on energy consumption quantities and emission factors, which lack precision. This study employs the low emissions analysis platform (LEAP) model, utilizing a "bottom-up" modeling approach combined with scenario analysis to predict and analyze the energy demand and related emissions in the transportation industry. Compared to previous studies, the methodological framework proposed in this research offers higher precision and can explore energy-saving and emission-reduction pathways for different modes of transport, providing a valuable energy forecasting tool for transport policy formulation in other regions. The forecast results indicate that under the business-as-usual (BAU) scenario, by 2049, the energy consumption and related emissions in Shaanxi Province's transportation industry are expected to increase by 1.15 to 1.85 times compared to the baseline year. In the comprehensive (CP) scenario, the industry is projected to reach a carbon peak around 2033. The study also finds that energy consumption and emissions predominantly originate from private passenger vehicles, highway freight, and civil aviation passenger, which have the greatest potential for emission reduction under the transport structure optimized (TSO) scenario. Therefore, policymakers should consider regional development characteristics, combine different transportation modes, and specifically analyze the emission reduction potential of the transportation industry in various regions, formulating corresponding reduction policies accordingly., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective: To investigate associations between air particulate matter of ≤2.5 μm in diameter (PM 2.5 ) and ovarian cancer., Design: County-level ecological study., Setting: Surveillance, epidemiology, and end results from a collection of state-level cancer registries across 744 counties. Data from the Environmental Protection Agency's network for PM 2.5 monitoring was used to calculate trailing 5- and 10-year PM 2.5 county-level values. County-level data on demographic characteristics were obtained from the American Community Survey., Population: A total of 98 751 patients with histologically confirmed ovarian cancer as a primary malignancy from 2000 to 2016., Methods: Generalised linear regression models were developed to estimate the association between PM 2.5 and PM 10 levels, over 5- and 10-year periods of exposure, and ovarian cancer risk, after accounting for county-level covariates., Main Outcome Measures: Risk ratios for associations between ovarian cancer (both overall and specifically epithelial ovarian cancer) and PM 2.5 levels., Results: For the 744 counties included, the average PM 2.5 level from 1990 through 2018 was 11.75 μg/m 3 (SD = 3.7) and the average PM 10 level was 22.7 μg/m 3 (SD = 5.7). After adjusting for county-level covariates, the overall annualised ovarian cancer incidence was significantly associated with increases in 5-year PM 2.5 (RR = 1.11 per 10 units (μg/m 3 ) increase, 95% CI 1.06-1.16). Similarly, when the analysis was limited to epithelial cell tumours and adjusted for county-level covariates there was a significant association with trailing 5-year PM 2.5 exposure models (RR = 1.12 per 10 units increase, 95% CI 1.08-1.17). Likewise, 10-year PM 2.5 exposure was associated with ovarian cancer overall and with epithelial ovarian cancer., Conclusions: Higher county-level ambient PM 2.5 levels are associated with 5- and 10-year incidences of ovarian cancer, as measurable in an ecological study., (© 2023 John Wiley & Sons Ltd.)

Brazil has historically invested few resources in its transport infrastructure, leaving gaps and reducing its efficiency. The country presents a high dependence on road transport, which results in increased operational costs and higher greenhouse gas (GHG) emissions. Consequently, the performance of cargo transportation in Brazil has been deteriorating, accompanied by a rise in the consumption of fossil fuels and noteworthy levels of GHG emissions. This article assesses the carbon intensity of soybean transport operations within Brazil. Utilizing a network equilibrium model, this study estimated the soybean transportation flows that minimize the total cost of transporting this product across the origins and destinations within the grain handling system. The modeling also calculated the GHG emissions in transportation. The results show that the transportation of soybeans produced 2.74 million tonnes of CO2 equivalent annually, with road transport accounting for 81.7% of these emissions. The state of Mato Grosso, responsible for 44.08 kg CO2 equivalent per tonne of soybeans transported, contributed almost 49% of the total emissions due to the extensive distances involved. In contrast, states like Paraná and Rio Grande do Sul, located closer to southern ports, exhibited the lowest emissions, with rates of 11.55 kg CO2 eq/t and 12.52 kg CO2 eq/t, respectively. The analysis highlights the significant potential for reducing GHG emissions by increasing the use of rail and barge transport, particularly in high-emission regions such as Mato Grosso., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

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.)

Odor pollution is the biggest source of complaints from citizens concerning environmental issues after noise. Often, the need for corrective actions is evaluated through simulations performed with atmospheric dispersion models. To save resources, air pollution control institutions perform a first-level odor impact assessment, for screening purposes. This is often based on Gaussian dispersion models (GDM), which does not need high computational power. However, their outputs tend to be conservative regarding the analyzed situation, rather than representative of the real in-site conditions. Hence, regulations and guidelines adopted at an institutional level for authorization/control purposes are based on Lagrangian particle dispersion models (LPDM). These models grant a more accurate simulation of the pollutants' dispersion even if they are more demanding regarding both technical skills and computing power. The present study aims to increase the accuracy of screening odor impact assessment by identifying the correlation function of the outputs derived from the two simulation models. The case study is placed in northern Italy, where a single-point source, with various stack heights, was considered. The case study is placed in northern Italy, where a single-point source, with various stack heights, was considered. The obtained correlation functions allow the practitioner to have a more accurate first-level odor impact assessment, to save time for training, and to reduce the site-specific meteorological data before proceeding with the simulation. The identified functions could allow institutions to estimate the results that would have been forecasted with the application of the more complex LPDM, applying, however, the much simpler GDM. This solution grants an accurate tool which can be used to address citizens' concerns while saving workforce and technical resources. Limitations are related to the specificity of the method regarding type sources, orography, and meteorological conditions. Comparison with other screening tools is also presented and discussed., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Although previous research has reached agreement on the significant impact of particulate matter (PM 2.5 ) on respiratory infectious diseases, PM 2.5 acts as an aggregation of miscellaneous pollutants and the individual effect of each component has not been examined. Here, we investigate the effects of PM 2.5 components, including black carbon (BC), organic carbon (OC), sulfate ion (SO 4 ), dust, and sea salt (SS), on the morbidity and mortality of the recent respiratory disease, i.e. COVID-19. The daily data of 236 countries and provinces/states (e.g., in the United States and China) worldwide during 2020-2022 are utilized. To derive the pollutant-specific causal effects, optimal instrumental variables for each pollutant are selected from a large set of atmospheric variables. We find that one µg/m 3 increase in OC increases the number of cases and death by about 3% to 6% from the mean worldwide during a lag of one day up to three days. Our findings remain consistent and robust when we change control variables such as the flight index and weather proxies, and also when applying a sine transformation to the positivity and death rate. When analyzing health effects among different areas, we find stronger impact in China, for its higher local OC concentration, as opposed to the impact in the United States. Health benefits from PM 2.5 pollution reduction are comparatively high for developed regions, yet decreases in cases and deaths number are rather overt in less developing regions. Our research provides inspiration and reference for dealing with other respiratory diseases in the post-pandemic era., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

The EAACI Guidelines on the impact of short-term exposure to outdoor pollutants on asthma-related outcomes provide recommendations for prevention, patient care and mitigation in a framework supporting rational decisions for healthcare professionals and patients to individualize and improve asthma management and for policymakers and regulators as an evidence-informed reference to help setting legally binding standards and goals for outdoor air quality at international, national and local levels. The Guideline was developed using the GRADE approach and evaluated outdoor pollutants referenced in the current Air Quality Guideline of the World Health Organization as single or mixed pollutants and outdoor pesticides. Short-term exposure to all pollutants evaluated increases the risk of asthma-related adverse outcomes, especially hospital admissions and emergency department visits (moderate certainty of evidence at specific lag days). There is limited evidence for the impact of traffic-related air pollution and outdoor pesticides exposure as well as for the interventions to reduce emissions. Due to the quality of evidence, conditional recommendations were formulated for all pollutants and for the interventions reducing outdoor air pollution. Asthma management counselled by the current EAACI guidelines can improve asthma-related outcomes but global measures for clean air are needed to achieve significant impact., (© 2024 European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.)

Objectives: Small-for-gestational-age (SGA) and large-for-gestational-age (LGA) births are major adverse birth outcomes related to newborn health. In contrast, the association between ambient air pollution levels and SGA or LGA births has not been investigated in Japan; hence, the purpose of our study is to investigate this association., Methods: We used birth data from Vital Statistics in Japan from 2017 to 2021 and municipality-level data on air pollutants, including nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), photochemical oxidants, and particulate matter 2.5 (PM 2.5 ). Ambient air pollution levels throughout the first, second, and third trimesters, as well as the whole pregnancy, were calculated for each birth. The association between SGA/LGA and ambient levels of the air pollutants was investigated using crude and adjusted log-binomial regression models. In addition, a regression model with spline functions was also used to detect the non-linear association., Results: We analyzed data from 2,434,217 births. Adjusted regression analyses revealed statistically significant and positive associations between SGA birth and SO 2 level, regardless of the exposure period. Specifically, the risk ratio for average SO 2 values throughout the whole pregnancy was 1.014 (95% confidence interval [CI] 1.009, 1.019) per 1 ppb increase. In addition, regression analysis with spline functions indicated that an increase in risk ratio for SGA birth depending on SO 2 level was linear. Furthermore, statistically significant and negative associations were observed between LGA birth and SO 2 except for the third trimester., Conclusions: It was suggested that ambient level of SO 2 during the pregnancy term is a risk factor for SGA birth in Japan., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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.)

This work presents the first comprehensive assessment of PM pollution sources in Dushanbe, Tajikistan. A total of 138 PM 2.5 samples were collected during 2015-2016 and 2018-2019 and were analyzed through gravimetric, ED-XRF, and multi-wavelength absorption techniques. The results show that PM 2.5 concentrations were substantially higher than the European annual limit value and WHO Air Quality Guidelines annual average value, with an average of 90.9 ± 68.5 μg m -3 . The PMF application identified eight sources of pollution that influenced PM 2.5 concentration levels in the area. Coal burning (21.3%) and biomass burning (22.3%) were the dominant sources during the winter, while vehicular traffic (7.7%) contributed more during the warm season. Power plant emissions (17.5%) showed enhanced contributions during the warm months, likely due to high energy demand. Cement industry emissions (6.9%) exhibited significant contribution during the cold period of 2018-2019, while soil dust (11.3%) and secondary sulphates (11.5%) displayed increased contribution during the warm and cold months, respectively. Finally, waste burning (1.5%) displayed the lowest contribution, with no significant temporal variation. Our results highlight the significant impact of anthropogenic activities, and especially the use of coal burning for energy production (both in power plants and for residential heating), and the significant contribution of biomass burning during both warm and cold seasons., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The increasing number of vehicles are emitting a large amount of particles into the atmosphere, causing serious harm to the ecological environment and human health. This study conducted the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) to investigate the emission characteristics of particle number (PN) of China-VI gasoline vehicles with different gasoline. The gasoline with lower aromatic hydrocarbons and olefins reduced particulate matter (PM) and PN emissions by 24% and 52% respectively. The average PN emission rate of the four vehicles during the first 300 s (the cold start period) was 7.2 times that of the 300 s-1800s. Additionally, because the particle transmission time and instrument response time, the test results of instantaneous emissions of PN were not synchronized with vehicle specific power (VSP). By calculating the Spearman correlation coefficient between pre-average vehicle specific power (PAVSP) and the test results of PN instantaneous emissions, the delay time was determined as 10s. After the PN emissions results were corrected, the PN emissions were found to be more related to VSP. By analyzing the influence of driving status on emission, this study found that vehicles in acceleration mode increased PN emissions by 76% compared to those in constant speed mode., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Odor pollution, also referred to as odor nuisance, is a growing environmental concern that is significantly associated with mental health. Once emitted into the air, the concentration of odorous substances varies considerably with wind conditions, leading to difficulties in timely sampling. In the present study, we employed selected ion flow tube mass spectrometry (SIFT-MS) to measure 22 odor-producing molecules continuously in an urban-rural complex city. In addition, we applied statistical analyses, principal component analysis (PCA), and a conditional probability function (CPF) to the datasets obtained from SIFT-MS to identify the odor characteristics at two study sites. At site A, odorants related to livestock farming and industry showed high factor loadings on principal components (PCs) from the PCA. In contrast, we estimated that the odorous gaseous chemicals affecting site B were closely related to sewage treatment and municipal solid waste disposal. Similar CPF patterns of grouped substances from the PCA supported the association between potential odor sources and specific odorants at site B, which helped estimate possible source locations. Consequently, our findings indicate that continuous monitoring of odorous substances using SIFT-MS can be an effective way to provide sufficient information on odor-producing molecules, leading to the clear identification of odor characteristics despite the high variability of odorous substances., (© 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.)

Of major interest, especially in city environments, and increasingly inside vehicles or industrial plants, is the drive to reduce human exposure to nitrogen oxides (NO x ). This trend has drawn increasing attention to filtration, which has developed remarkably owing to the capabilities of recently developed mathematical models and novel filter concepts. This paper reports on the study of the kinetic modelling of adsorption of nitrogen dioxide (NO 2 ), collected from the tailpipe of a diesel engine, reacting to calcium nitrate salt (Ca(NO 3 ) 2 ) on a surface flow filter consisting of a coating of fine ground limestone or marble (CaCO 3 ) in combination with micro-nanofibrillated cellulose (MNFC) acting as binder and humectant applied onto a multiply recycled newsprint substrate. The coating and substrate are both porous, but on different pore size scales, with the coating having significantly lower permeability. To maximise gas-coating contact, therefore, the coating deposition is pixelated, achieved by pin coating. An axially dispersed gaseous plug flow model (dispersion model) was used to simulate the transport within the coating pore network structure, following earlier flow modelling studies, and a kinetic reaction model was used to examine NO 2 to NO 3 - conversion in correlation with experimental results. Modelling results indicate a 60.38% conversion of exposed NO 2 gas to Ca(NO 3 ) 2 under the specific conditions applied, with an absolute relative error between the predicted and experimentally estimated value being 0.81%. The model additionally enabled a prediction of effects of changing parameters over a limited perturbation range, thus assisting in predicting filter element consumption, with attention given to the active component CaCO 3 surface as a function of particle size in relation to the gas contact exchange, promoting the reaction over time. It is intended that the Ca(NO 3 ) 2 formed from the reaction can go on to be used as a value-added fertiliser, thus contributing to circular economy., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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.)

Objective: The aim of the study is to examine whether preoprerative exposure to air pollution is associated with mortality after a major cancer surgery., Methods: All patients who underwent major cancer surgeries after hospitalization between January 1, 2016, to December 31, 2020, were included., Results: In total, 244,766 patients who underwent major cancer surgeries were included. Both 0.001-ppm increase in sulfur dioxide and 0.1-ppm increase in carbon monoxide were associated with a 7% increase in 90-day mortality rate after a major cancer surgery. Furthermore, a 0.001-ppm increase in sulfur dioxide, 0.1 ppm increase in carbon monoxide, and 1 μg/m 3 increase in particulate matter 2.5 were associated with a 4%, 3%, and 1% increase in 1-year all-cause mortality rate after a major cancer surgery., Conclusions: Preoperative exposure to air pollution was associated with an increased risk of mortality in patients after major cancer surgery., Competing Interests: Song and Oh have no relationships/conditions/circumstances that present potential conflict of interest., (Copyright © 2023 American College of Occupational and Environmental Medicine.)

The number of cars is increasing every year and the environmental aspects of transport are becoming a hot topic. The spatial and temporal patterns of motor vehicle carbon monoxide (CO) emissions are still unclear due to the unbalanced economic development and heterogeneous geographic conditions of China. With the objective of realizing a reduction in motor vehicle CO emissions, his study explores the transport carbon emission reduction pathways of China from motor vehicle CO emission. Firstly, the entropy method is adopted to comprehensively evaluate the CO emissions from motor vehicles in each province; secondly, the development of a Geographically and Temporally Weighted Regression (GTWR) model facilitates the examination of the spatiotemporal dynamics pertaining to the influencing factors of motor vehicle CO emissions within each province.; finally, the characteristics of motor vehicle CO emissions in ETS pilot areas and non-ETS pilot areas are compared. The results show that: (1) After the completion of the six ETS pilot areas in 2011, the CO emission from motor vehicles is reduced by 18% compared with 2010.(2)The entropy method shows that the largest CO emissions from motor vehicles are from Beijing, Shanghai, Guangdong and other provinces with high economic levels.(3) The results of the GTWR model show that the positive effects of economic level, population size, road mileage intensity and motor vehicle intensity on motor vehicle CO emissions are decreasing year by year. The negative effect of metro line intensity on CO emission decreases year by year. This study can help decision makers to identify the high emission areas, understand the influencing factors, and formulate emission reduction measures to achieve the purpose of carbon emission reduction in transport., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Protecting people from air pollution is an important task for developing countries. Over the past several decades, different types of policies have been enacted in developing countries to improve air quality, with policy impact analysis being conducted as well. As for China, while there is some existing literature on the impact of short-term action on air quality, there is much less research to investigate the roles of long-term air quality control mechanism on air quality change. This paper uses the "2 + 26" city cluster in China as a case study area to particularly analyze the roles played by short-term campaigns that compose the Winter Action Plan and a long-term mechanism referred to as the environmental accountability system in pollution control. Based on econometric and descriptive statistical analyses, it is found that the implementation of the Winter Action Plan has significantly improved the air quality, and the environmental accountability system seems to have exerted an impact, as cities with better air quality appear to establish a more stringent environmental accountability system. The paper provides the following insights for other developing countries: short-term campaigns can quickly reduce pollution and protect human health, while long-term mechanisms are needed to ensure sustainable environmental protection., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study attempts to examine the morphological, elemental and physical characteristics of PM 10 over the Indian Himalayan Region (IHR) using FTIR and scanning electron microscopy-energy dispersive X-ray (SEM-EDX) analysis. The study aimed at source identification of PM 10 by exploring the inorganic ions, organic functional groups, morphology and elemental characteristics. The pollution load of PM 10 was estimated as 63 ± 22 μg m -3 ; 53 ± 16 μg m -3 ; 67 ± 26 μg m -3 and 55 ± 11 μg m -3 over Mohal-Kullu, Almora, Nainital and Darjeeling, respectively. ATR-FTIR spectrum analysis revealed the existence of inorganic ions (SiO 4 4- , TiO 2 , SO 4 2- , SO 3 - , NO 3 - , NO 2 - , CO 3 2- , HCO 3 - , NH 4 + ) and organic functional groups (C-C, C-H, C=C, C≡C, C=O, N-H, C≡N, C=N, O-H, cyclic rings, aromatic compounds and some heterogeneous groups) in PM 10 which may arise from geogenic, biogenic and anthropogenic sources. The morphological and elemental characterization was performed by SEM-EDX, inferring for geogenic origin (Al, Na, K, Ca, Mg and Fe) due to the presence of different morphologies (irregular, spherical, cluster, sheet-like solid deposition and columnar). In contrast, particles having biogenic and anthropogenic origins (K, S and Ba) have primarily spherical with few irregular particles at all the study sites. Also, the statistical analysis ANOVA depicts that among all the detected elements, Na, Al, Si, S and K are site-specific in nature as their mean of aw% significantly varied for all the sites. The trajectory analysis revealed that the Uttarakhand, Jammu and Kashmir, the Thar Desert, Himachal Pradesh, Pakistan, Afghanistan, Nepal, Sikkim, the Indo-Gangetic Plain (IGP) and the Bay of Bengal (BoB) contribute to the increased loading of atmospheric pollutants in various locations within the IHR., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The effects of air pollution on health are gaining increasing research interest with limited data on skin alterations available. It was suggested that air pollution is a trigger factor for sensitive skin (SS). However, this data was based on surveys with a lack of experimental data. SS is related to altered skin nerve endings and cutaneous neurogenic inflammation. TTe present study was to assess the in vitro effect of particulate matter (PM) on epidermis and nerve ending homeostasis. PM samples were collected according to a validated protocol. Reconstructed human epidermis (RHE, Episkin®) was exposed to PM and subsequently the supernatants were transferred to a culture of PC12 cells differentiated into sensory neurons (SN). Cell viability, axonal growth and neuropeptide-release were measured. The modulation of the expression of different inflammatory, keratinocytes differentiation and neurites growth markers was assessed. PM samples contained a high proportion of particles with a size below 1 μm and a complex chemical composition. Transcriptomic and immunohistochemical analyses revealed that PM altered keratinocytes terminal differentiation and induced an inflammatory response. While viability and functionality of the SN were not modified, their outgrowth was significantly decreased after incubation with PM-exposed Episkin® supernatants. This was closely related to the modification of nerve growth factor/semaphorin 3A balance. This study showed that air pollutants have negative effects on keratinocytes and sensory nerve endings including inflammatory responses. These effects are probably involved in the SS pathophysiology and might be involved in inflammatory skin disorders., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

This study investigates the impact of ambient air pollution on housing prices in Warsaw, Poland, by examining spatial dependencies. The high concentration of particulate matter (PM 10 , PM 2.5 and PM 1 ) is expected to reduce real estate values. Using a hedonic model with approximately 15,000 observations and a spatial error model, we did not find evidence of this impact. Standard and premium housing submarkets differ in price determinants, but both are insensitive to environmental issues. This could be explained by the lack of comprehensive intra-urban historical information on air pollution, which limits investors' rationality and their ability to properly value real estate based on environmental issues. Additionally, measurement and aggregation issues, along with low pollution variability within the city, may contribute to the insignificance of this information in real estate sales prices. Our empirical research confirms a strong link between air pollution and weather conditions within the city, where low temperatures and low-speed southern winds worsen contamination levels, while high temperatures and westerly winds improve air quality. Furthermore, we find that incorporating pollution data using PM yearly mean concentration works better in modelling than the PCA-reduced air pollution index., (© 2024. The Author(s).)

Ag/N-TiO2 aerogel photocatalyst (Ag/N-TiO2 ACP) was prepared by the sol–gel method, using AgNO3 and urea as the sources of silver and nitrogen, respectively. The surface of the photocatalyst was modified using an alcoholic solution of ethyl orthosilicate (20% v/v) during the drying process under atmospheric pressure. Ag/N-TiO2 ACP, microcrystalline cellulose (MCC) and polyvinyl butyral (PVB) were combined by electrostatic spinning to prepare Ag/N-TiO2 ACP/MCC composite fibers (Ag/N-TiO2/MCC-CFs). Ag/N-TiO2/MCC-CFs were then composited with pulp fibers to prepare a photocatalytic paper (Ag/N-TiO2/MCC CFs-paper) with a wide spectral response range. Ag/N-TiO2 ACP has a high specific surface area (288.92 m2/g) and good antibacterial activity (no obvious colony after 2 h under simulated sunlight). Scanning electron microscopy images showed Ag/N-TiO2/MCC-CFs anchored in the pulp fibers. The composite photocatalytic paper exhibited good antibacterial activity against Escherichia coli (> 97% growth inhibition under simulated sunlight for 2 h) and Staphylococcus aureus (> 91% growth inhibition under simulated sunlight for 2 h). The photocatalytic paper also showed good photocatalytic activity and stability for the degradation of liquid-phase organic pollutants (MB, adsorption-photocatalytic degradation ~ 100%) and volatile organic pollutants (HCHO, adsorption-photocatalytic degradation 57.19%) under light irradiation. This study provides a simple and green method for preparation of a novel paper-based material with effective antibacterial and photocatalytic activity that has potential for photocatalytic sewage purification and degradation of indoor air pollutants, such as HCHO. [ABSTRACT FROM AUTHOR]

In Europe mainly at winter season the PM levels exceed air quality limits, which correlated with the operation of solid-fired boilers. More and more people are returning to using these devices due to energy shortage caused by the pandemic and regional conflicts. In addition, the phenomena of co-burning fuels and municipal waste in residential boilers in primarily fuel poverty households increases further the amount of pollutants in the atmosphere. This study aims to correlate the quantity and quality of air pollutants with the type of fuel (wood and wastes) burned. Combustion experiments were conducted using oak fuel mixed with three waste groups: (1) plastics (PP, HDPE, PET); (2) textiles (polyester—PES, cotton—COT); and (3) papers (cardboard—CARD, glossy coated paper—GCP, 84C/PAP). The addition of waste to wood fuel altered the morphology of emitted particles. While waste burning doesn't always increase particle quantity, it significantly raises PAH concentrations. A strong relationship exists between waste type, particle morphology, and PAH quality, where with lower molecular weight PAHs linked to tar agglomerates and higher ones to soot agglomerates with inorganic crystals. [ABSTRACT FROM AUTHOR]

Pollution of air, water, and soil is a pressing global issue with significant implications for environmental and human health. This research paper explores the various methods and technologies employed for the monitoring and remediation of pollutants in these three crucial environmental compartments. It discusses the challenges associated with pollution, the importance of effective monitoring, and the diverse strategies available for pollutant remediation. By synthesizing current research and technologies, this paper aims to provide insights into the state-of-the-art approaches for addressing pollution and promoting environmental sustainability [ABSTRACT FROM AUTHOR]

China is currently focusing on the cooperative control of air pollution and CO2 emissions, as well as the mitigation of economic policy uncertainty (EPU). By using panel data from 282 cities spanning from 2003 to 2017 and a newly constructed city-level EPU index, a spatial Durbin, two-way fixed-effects model is employed, with the aim of estimating the impact of EPU on the synergistic emissions intensity (SEI) of air pollutants and CO2. Additionally, this paper investigates the potential channels through which EPU influences SEI. It also explores how pressures related to environmental protection and economic development affect the impact of EPU on SEI. The results indicate that a unit increase in EPU will result in a rise in the SEI of local cities, adjacent cities, and total cities by 930.9%, 69,162.7%, and 70,093.6%, respectively. Moreover, the channel analysis suggests that EPU exacerbates SEI by undermining the upgrading of the industrial structure, augmenting industrial structure distortion, and escalating labor market distortion. Furthermore, the effect of EPU on SEI may be lessened by an increase in environmental protection pressure, while an increase in economic development pressure may exert a positive influence. Finally, this paper concludes by recommending that policymakers should prioritize the maintenance and stability of economic policies, facilitate the advancement of the industrial structure, enhance the efficiency of labor resource allocation, and underscore the significance of managing urban air pollution and CO2 emissions. [ABSTRACT FROM AUTHOR]

The Omicron pandemic broke out in Shanghai in March 2022, and some infected people spread to some cities in the Yangtze River Delta (YRD) region. To achieve the dynamic zero-COVID target as soon as possible, Shanghai and nine cities that were heavily affected by Shanghai implemented the lockdown measures. This paper aims to quantify the impact of the lockdown on air quality and human health. A difference-in-difference (DID) model was first used to measure the impact of the lockdown on air quality in these ten cities. Based on the results of the DID model, we estimated the PM2.5-related health and economic benefits using the concentration–response function and the value of statistical life method. Results showed that the lockdown has reduced the concentrations of PM2.5, PM10, SO2, NO2, and CO by 9.87 μg/m3, 17.31 μg/m3, 0.75 μg/m3, 9.03 μg/m3, and 0.07 mg/m3, respectively. The number of avoided premature deaths due to PM2.5 reduction was estimated to be 35,342. The resulting economic benefits totaled 18.86 billion US dollars. We investigated the reasons for the air quality improvement in these ten cities and found the "3 + 11" policy has had a great impact on air quality. Compared with the first COVID-19 lockdown in early 2020, the effect of the lockdown in 2022 was smaller. These findings demonstrated that reductions in anthropogenic emissions would achieve substantial air quality improvement and health benefits. This paper re-emphasized continuous efforts to improve air quality are essential to protect public health. [ABSTRACT FROM AUTHOR]

One of the main global problems related to the environment is air pollution which not only affects the environment and the ecosystem but also affects human health. Many countries of the world did research on how to reduce air pollution so that the concentrations do not exceed the permissible thresholds which are set at that level so that neither human health nor the environment are exposed to further damage. One of the issues regarding air pollution is the concentrations of PM particles 2.5 and 10, PM particles are a big problem in developing countries such as the Republic of North Macedonia. The research paper was focused in processing the data that has been collected for daily averages of PM 2,5 and PM10 particles that contribute in air pollution. All analyzes and processing of the results are done in compliance with the current laws of the Republic of North Macedonia and with Directive 2008/50/EC, all for µg/m3. In the city of Skopje, numerous exceedances of the permissible thresholds of PM particles 2.5 and 10 were recorded at the two measurement stations where the results were obtained for the months of January, February, March, April, May and June of 2023. [ABSTRACT FROM AUTHOR]

This systematic review assessed to evaluate the potential correlation between oral health and air pollution. To the best of the authors' knowledge, this is the first systematic review endeavoring to compare air pollution and oral health. A systematic search was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) statement and employed the PICO(S) approach (Patient or Population, Intervention, Control or Comparison, Outcome, and Study types). The search was limited to English-language articles, and publications within a 15-year timeframe were included in the electronic search. A comprehensive search was conducted across PubMed, Scopus, Embase, and Web of Science databases, spanning the years 2008 to 2023, resulting in a total of 4983 scientific articles. A final selection of 11 scientific papers was made based on their study type and the specific air pollutants examined. The selected papers analyzed various air pollutants associated with health-related diseases, including Ozone, Nitrogen Dioxide, Nitrogen Monoxide, Carbon Monoxide, sulfur dioxide, and particulate matter. Three out of eleven of the reviewed studies assert a strong correlation between air pollutants and oral diseases, specifically periodontitis. However, the exact biological mechanisms underlying this correlation do not seem to be fully understood, indicating the need for further comprehensive investigation in this regard. Dentists can contribute to the collective effort by educating their patients about the oral health implications of air pollution, thereby supporting initiatives aimed at promoting environmental and health sustainability. [ABSTRACT FROM AUTHOR]

The civil aircraft cabin is enclosed and highly occupied, making it susceptible to a decline in indoor environmental quality. The environmental quality of civil aircraft cabins not only depends on objective factors such as temperature, relative humidity, and the presence of air pollutants such as carbon dioxide (CO2), carbon monoxide (CO), ozone (O3), particle matter (PM), and volatile organic compounds (VOCs) but also the subjective factors pertaining to the perceptions and health symptoms of passengers and crew. However, few studies have thoroughly examined the air quality and thermal comfort parameters that are measured during in-flight testing in airplane cabins, as well as the passengers' subjective perceptions. In order to evaluate the in-flight thermal comfort and air quality status, this study conducted a review of the recent literature to compile data on primary categories, standard limits, and distribution ranges of in-flight environmental factors within civil aircraft cabins. Following a search procedure outlined in this paper, 54 papers were selected for inclusion. Utilizing the Monte Carlo method, the Predicted Mean Vote (PMV) distributions under different exercise intensities and clothing thermal resistance were measured with the in-cabin temperature and humidity from in-flight tests. Recommendations based on first-hand data were made to maintain the relative humidity in the cabin below 40%, ensure wind speed remains within the range of 0–1 m/s, and regulate the temperature between 25–27 °C (for summer) and 22–27 °C (for winter). The current estimated cabin air supply rate generally complies with the requirements of international standards. Additionally, potential carcinogenic and non-carcinogenic risks associated with formaldehyde, benzene, tetrachloroethylene, and naphthalene were calculated. The sorted data of in-flight tests and the evaluation of the subjective perception of the occupants provide an evaluation of current cabin thermal comfort and air quality status, which can serve as a reference for optimizing indoor environmental quality in future generations of civil aircraft cabins. [ABSTRACT FROM AUTHOR]

Research on the quality of the air in rural areas is essential for determining base emissions of air pollutants, evaluating the effects of dust pollutants particular to rural areas, modeling the dispersion of pollutants, and developing appropriate pollution mitigation systems. The absence of a systematic review based on the assessment of air quality levels in agricultural settings based on integrated weather variables and air pollutants in the literature draws attention to the deficiencies and the necessity of further research in this area. Hence, our study aimed to develop an Arduino monitoring system with related sensors to acquire some air pollutants and weather parameters. Additionally, we proposed an innovative solution to compare air quality levels by suggesting a new criterion called an integrated indicator for air quality assessment (IAQA). It was created based on the weighted average method to combine the investigated air pollutants and weather parameters. This criterion was evaluated while conducting field measurements in 29 environmentally different agricultural regions located within the Kingdom of Saudi Arabia. To determine the integrated indicator, all the values of the variables were normalized between 0 and 1. The agricultural setting with the lowest integrated indicator was the best environmentally. The lowest and highest values of the integrated indicator ranged from 37.03% and 66.32%, respectively, with an arithmetic average of 48.24%. The developed criterion can change its value depending on the change in the weight value of the variables involved, and it is suitable for application to any other agricultural or non-agricultural setting to evaluate the pollution level in the air. Although similar research has been published, this paper presents novelty findings based on integrated values of air pollutants and weather variables for defining a new criterion called IAQA. Additionally, this paper presents original results for air pollutants and weather aspects in different agricultural settings. [ABSTRACT FROM AUTHOR]

In recent decades, numerous studies have indicated the substantial role semiconductors could play in photocatalytic processes for environmental applications. Materials that contain a semiconductor as a photocatalyst have a semi-permanent capacity for removing harmful gases from the ambient air. In this paper, the focus is on TiO2. Heterogeneous photocatalysis using TiO2 leads to the degradation of NO/NO2, benzene, toluene, and other priority air pollutants once in contact with the semiconductor surface. Preliminary evidence indicates that TiO2-containing construction materials and paints efficiently destroy the ozone precursors NO and NO2 by up to 80% and 30%, respectively. Therefore, the development of innovative coatings containing TiO2 as a photocatalyst was in the foreground of research activities. The aim of this was for coatings to be used as building and construction materials, mainly outdoors, e.g., on building façades on high-traffic roads for the degradation of priority air pollutants (NOx and volatile organic compounds) in the polluted urban atmosphere. Though there are advantages connected with the application of TiO2, due to its band gap of 3.2 eV, these are limited. TiO2 is effective only in the UV region (ca. 5%) of the solar spectrum with wavelengths λ < 380 nm. Hence, efforts are made here, as in many research studies, to dope TiO2 with transition metals to increase its activity using visible light, which will extend its application to indoor environments. In our studies, experiments were conducted with 0.1% (w/w) and 1% (w/w) Mn-TiO2 admixtures, and the ability of the modified photocatalysts to degrade NO by both solar and indoor illumination was evaluated. The surface chemistry at the air/catalyst interface, governed by the photoelectric characteristics of TiO2 and the formation of reactive oxygen species with co-occurring redox reactions, is reviewed in this paper. The factors affecting the application of TiO2 for the degradation of priority air pollutants as single compounds or mixtures are discussed. We investigated, particularly, the degradation of mixtures of priority compounds at typical concentrations in ambient air and confined spaces. This is a realistic approach, because pollutants are present as mixtures, rather than as individual compounds in ambient and indoor air. Moreover, organic polymers as paint constituents were found to be the primary source for carbonyl formation, e.g., formaldehyde, acetaldehyde, etc., during the heterogeneous photocatalytic processes conducted on TiO2-enriched coatings. [ABSTRACT FROM AUTHOR]

Nitrogen oxides (NOx = NO + NO2) are air pollutants which are co-emitted with CO2 during high-temperature combustion processes. Monitoring NOx emissions is crucial for assessing air quality and for providing proxy estimates of CO2 emissions. Satellite observations, such as those from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5P satellite, provide global coverage at high temporal resolution. However, satellites measure only NO2 , necessitating a conversion to NOx. Previous studies have applied a constant NO2 -to- NOx conversion factor. In this paper, we develop a more realistic model for NO2 -to- NOx conversion and apply it to TROPOMI data of 2020 and 2021. To achieve this, we analysed plume-resolving simulations from the MicroHH large-eddy simulation model with chemistry for the Bełchatów (PL), Jänschwalde (DE), Matimba (ZA) and Medupi (ZA) power plants, as well as a metallurgical plant in Lipetsk (RU). We used the cross-sectional flux method to calculate NO , NO2 and NOx line densities from simulated NO and NO2 columns and derived NO2 -to- NOx conversion factors as a function of the time since emission. Since the method of converting NO2 to NOx presented in this paper assumes steady-state conditions and that the conversion factors can be modelled by a negative exponential function, we validated the conversion factors using the same MicroHH data. Finally, we applied the derived conversion factors to TROPOMI NO2 observations of the same sources. The validation of the NO2 -to- NOx conversion factors shows that they can account for the NOx chemistry in plumes, in particular for the conversion between NO and NO2 near the source and for the chemical loss of NOx further downstream. When applying these time-since-emission-dependent conversion factors, biases in NOx emissions estimated from TROPOMI NO2 images are greatly reduced from between - 50 % and - 42 % to between only - 9.5 % and - 0.5 % in comparison with reported emissions. Single-overpass estimates can be quantified with an uncertainty of 20 %–27 %, while annual NOx emission estimates have uncertainties in the range of 4 %–21 % but are highly dependent on the number of successful retrievals. Although more simulations covering a wider range of meteorological and trace gas background conditions will be needed to generalise the approach, this study marks an important step towards a consistent, uniform, high-resolution and near-real-time estimation of NOx emissions – especially with regard to upcoming NO2 -monitoring satellites such as Sentinel-4, Sentinel-5 and CO2M. [ABSTRACT FROM AUTHOR]

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This paper summarizes the results of a Special Issue focusing on the practical applications of model predictive control and other advanced control methods in the built environment. This Special Issue contains eleven publications and deals with various topics such as the virtual sensing of indoor air pollutants and prediction models for indoor air temperature and building heating and cooling loads, as well as local and supervisory control strategies. The last three publications tackle the predictive maintenance of chilled water systems. Most of these publications are field demonstrations of advanced control solutions or promising methodologies to facilitate the adoption of such control strategies, and they deal with existing buildings. The Special Issue also contains two review papers that provide a comprehensive overview of practical challenges, opportunities, and solutions to improve building operations. This article concludes with a discussion of the perspectives of advanced controls in the built environment and the increasing importance of data-driven solutions. [ABSTRACT FROM AUTHOR]

The accurate quantification of concentration fluctuations is crucial when evaluating the exposure to toxic, infectious, reactive, flammable, or explosive substances, as well as for the estimation of odor nuisance. However, in the field of Computational Fluid Dynamics (CFD), the industry currently relies predominantly on steady-state RANS turbulence models for simulating near-field pollutant dispersion, which are only capable of producing the time-averaged concentration field. This paper presents a regression relationship for calculating the standard deviation of the local concentration based on the mean concentration and the downstream distance from a point source, over a city-like surface, in the case of the wind direction perpendicular to the streets. The desired peak values and other statistical characteristics can be predicted by assuming a gamma distribution which is fitted based on the average and standard deviation. To obtain the regression function, a deep neural network model was used. The model was trained using timeresolved concentration data obtained from wind tunnel experiments. The validation results show that the concentration fluctuations predicted by the DNN-based model are in satisfactory agreement with the measurement data in terms of the skewness, the kurtosis, the median, and the peak concentrations. Furthermore, the present paper suggests a workflow for estimating the concentration fluctuations based on RANS CFD results, as well as recommendations for generating further training data for specific applications. [ABSTRACT FROM AUTHOR]

Environmental toxins pose significant threats to ecosystems and human health. Monitoring and assessing these toxins are crucial for effective environmental management and public health protection. Recently, plant species have garnered increasing attention as potential bioindicators for identifying and evaluating ecological toxins. Since plants often come into touch with harmful compounds in soil, water, and the atmosphere, they are particularly valuable for analyzing how human activities influence the terrestrial ecosystem, the aquatic system, and the atmosphere. This review paper emphasizes using plant species as a resource for tracking environmental pollution and analyzing contaminants. We focused on plants because they are significant indicators of soil, water, and air quality changes. Many plants have been used as bio-indicators to assess and predict pollution, toxicity, and environmental changes. These include Allium cepa, Vicia faba, Pisum sativum, Zea mays, Nicotiana tabacum, lichens, and mosses. The idea of bioindicators is discussed in the current paper, with a focus on plants as possible candidates for bioindicators for toxin assessment and related outcomes. [ABSTRACT FROM AUTHOR]

The special large-slope V-shaped structure of underwater tunnels changes the ventilation characteristics during tunnel construction, making the traditional experience limited. Therefore, it is urgent to study the influence of the special structure on the safety of the air environment during construction. In this paper, a series of small-scale experiments were conducted to investigate the ventilation characteristics of V-shaped tunnels. The coupled effects of ventilation parameters (distance of duct outlet from working face  L 0 , air velocity at the duct outlet  u 0 ) and structural characteristics (digging length  L d , slope of the uphill section  θ) were considered. The extreme slope of the V-shaped tunnel of 8% was considered. The flow field and pollutant transport law were determined by using CO as a tracer in the experiments. The results show that  u 0  has a positive impact on the air return velocity, while  L d  has a negative impact, and neither of the other two factors has a significant effect. The transport characteristics of CO in V-shaped tunnels differ from those in flat tunnels, with the former tending to cause unconventional areas of high pollutant concentrations in the horizontal sections. Furthermore, the correlations between CO concentration and distance, ventilation time, and the influence factors discussed in this paper are derived from the experimental results. The conclusions provide guidance for the construction of V-shaped tunnels to prevent air pollution in the construction environment and to improve the working conditions of laborers. Additionally, it can also enrich the ventilation experience in tunnel construction. [ABSTRACT FROM AUTHOR]

Per- and polyfluoroalkyl substances (PFAS), often referred to as "forever chemicals", are a class of man-made, extremely stable chemicals, which are widely used in industrial and commercial applications. Exposure to some PFAS is now known to be detrimental to human health. By virtue of PFAS long residence times, they are widely detected in the environment, including remote locations such as the Arctics, where the origin of the PFAS is poorly understood. It has been suggested that PFAS may be transported through contaminated waters, leading to accumulation in coastal areas, where they can be aerosolised via sea spray, thereby extending their geographical distribution far beyond their original source regions. The aim of this work is to investigate, for the first time, whether "forever chemicals" could be transported to areas considered to be pristine, far from coastal sites. This study was performed at the Amazonian Tall Tower Observatory (ATTO), a unique remote site situated in the middle of the Amazon rainforest, where a restricted PFAS, perfluorooctanoic acid (PFOA), was observed with concentrations reaching up to 2 pg/m 3 . A clear trend of increasing concentration with sampling height was observed and air masses from the south over Manaus had the highest concentrations. Atmospheric lifetime estimations, removal mechanisms supported by measurements at two heights (320 and 42 m above the rainforest), and concentration spikes indicated a long-range transport of PFOA to pristine Amazon rainforest. Potential sources, including industrial activities in urban areas, were explored, and historical fire management practices considered. This research presents the first measurements of PFAS in the atmosphere of Amazon rainforest. Remarkably, even in this remote natural environment, appreciable levels of PFAS can be detected. This study provides valuable insights into the long-range transport of the anthropogenic "forever chemical" into a remote natural ecosystem and should raise awareness of potential environmental implications., Competing Interests: Declaration of competing interest The authors declare 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.)

Background: Epidemiological studies of long-term exposure to outdoor air pollution have consistently documented associations with morbidity and mortality. Air pollution exposure in these epidemiological studies is generally assessed at the residential address, because individual time-activity patterns are seldom known in large epidemiological studies. Ignoring time-activity patterns may result in bias in epidemiological studies. The aims of this paper are to assess the agreement between exposure assessed at the residential address and exposures estimated with time-activity integrated and the potential bias in epidemiological studies when exposure is estimated at the residential address., Main Body: We reviewed exposure studies that have compared residential and time-activity integrated exposures, with a focus on the correlation. We further discuss epidemiological studies that have compared health effect estimates between the residential and time-activity integrated exposure and studies that have indirectly estimated the potential bias in health effect estimates in epidemiological studies related to ignoring time-activity patterns. A large number of studies compared residential and time-activity integrated exposure, especially in Europe and North America, mostly focusing on differences in level. Eleven of these studies reported correlations, showing that the correlation between residential address-based and time-activity integrated long-term air pollution exposure was generally high to very high (R > 0.8). For individual subjects large differences were found between residential and time-activity integrated exposures. Consistent with the high correlation, five of six identified epidemiological studies found nearly identical health effects using residential and time-activity integrated exposure. Six additional studies in Europe and North America showed only small to moderate potential bias (9 to 30% potential underestimation) in estimated exposure response functions using residence-based exposures. Differences of average exposure level were generally small and in both directions. Exposure contrasts were smaller for time-activity integrated exposures in nearly all studies. The difference in exposure was not equally distributed across the population including between different socio-economic groups., Conclusions: Overall, the bias in epidemiological studies related to assessing long-term exposure at the residential address only is likely small in populations comparable to those evaluated in the comparison studies. Further improvements in exposure assessment especially for large populations remain useful., (© 2024. The Author(s).)

Background: Existing epidemiological studies have indicated a correlation between air pollutants and the occurrence of mental disorders. However, it is difficult to estimate the causal relationship between the two because of the limitations of traditional epidemiological research. In our study, we aimed to extensively explore the causal relationship between five types of air pollutants and four types of mental disorders., Methods: Based on the IEU OPEN GWAS database, we performed a two-sample MR analysis. The primary analysis method utilized was the inverse variance weighted (IVW) method, supplemented by the MR-Egger method and the weighted median method. Additionally, we conducted sensitivity analyses with the Cochran's Q statistic method, the leave-one-out method, and the MR-Egger intercept. We chose at least 4 GWAS datasets for each of the four psychiatric diseases and conducted a meta-analysis of our results of the MR analysis., Results: The meta-analysis's findings demonstrated a causal link between depression and PM2.5 (OR=1.020, 95 %CI: (1.010,1.030), P=0.001). PM10 and schizophrenia are also causally related (OR=1.136, 95 %CI: (1.034,1.248), P=0.008). Nitrogen oxides and bipolar disorder have a causal relationship (OR=1.002, 95 %CI: (1.000,1.003), P=0.022). Nitrogen oxides and schizophrenia have a high causal association (OR=1.439, 95 %CI: (1.183,1.752), P<0.001)., Conclusion: This study observed a causal association between increased concentrations of PM2.5, PM10, and nitrogen oxides and the occurrence of depression, schizophrenia, and bipolar disorder. Our research findings have certain guiding implications for treating and preventing mental disorders., 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.)

Climate change and air pollution are major challenges facing the world today. Cold waves and air pollution significantly impact ischemic heart disease (IHD), but the extent of these effects at different altitudes remains unclear, especially their interactions. We collected daily meteorological, pollutant, and IHD hospitalization data from Xining and Xinxiang from 2016 to 2021. Using a time-stratified case-crossover approach, we fitted conditional Poisson regression models to assess the association between cold waves, PM 2.5 , and IHD hospitalizations and quantified their interactions. Additionally, we calculated the attributable fraction (AF) and attributable number (AN) of hospitalizations due to exposure to cold waves and medium to high-level PM 2.5 . We also performed stratified analyses by altitude, gender, and age. Both cold waves and PM 2.5 were positively associated with IHD hospitalization rates in Xining and Xinxiang, but the differences between the two regions were not significant. The relative risk of cold waves was 1.15 (1.07, 1.24) in Xining and 1.16 (1.11, 1.21) in Xinxiang. In Xining, there was an interaction between cold waves and different levels of PM 2.5 . We estimated the attributable fraction due to the joint exposure of cold waves and PM 2.5 to be 0.14-0.49 in Xining and 0.26-0.36 in Xinxiang. Older adults and males faced higher risks. This study highlights the importance of reducing PM 2.5 exposure and optimizing extreme weather warning systems and suggests further exploration of the impacts of individual behaviors and regional characteristics on IHD., 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.)

Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH 3 , CO, NO 2 , CH 4 , CO 2 , SO 2 , O 3 , PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Montaser Ramadan reports writing assistance was provided by University of Malaya. 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 Inc. All rights reserved.)

The construction of cascade reservoirs can interfere with the natural hydrologic cycles of basins, causing negative environmental effects such as altering the emission patterns of the Nitrous oxide (N 2 O), a potent greenhouse gas. To elucidate the impact of cascade reservoirs construction on river N 2 O emissions, we utilized the thin boundary model and the incubation experiments to estimate the N 2 O fluxes at the air-water interface and at the water-sediment interface of cascade reservoirs on the Yunnan-Guizhou Plateau, respectively. Additionally, we explored the influence of various factors, with particular emphasis on damming, on N 2 O emissions and production. Moreover, we identified the main pathways of N 2 O production and proposed management strategies to mitigate N 2 O emissions from cascade reservoirs. The findings revealed that N 2 O fluxes at the air-water interface and the water-sediment interface were 4.73 ± 1.32 μmol m -2 · d -1 and 15.56 ± 1.98 μmol m -2 · d -1 , respectively. Influenced by temperature, dissolved oxygen (DO), resource substances (active nitrogen substrates and dissolved organic carbon (DOC)) and reservoir properties (scale, hydraulic retention time (HRT), reservoir age, etc.), the N 2 O concentration and flux exhibited notable spatial heterogeneity, gradually increasing downstream. Temperature has a significant direct impact on N 2 O flux, as well as indirect effects through DO and resource chemicals. Furthermore, the correlation between dissolved oxygen utilization rate (AOU) and net N 2 O flux (ΔN 2 O) indicated that N 2 O emissions at the water-air interface were primarily attributable to nitrification, whereas those at the water-sediment interface were predominantly driven by denitrification. These findings not only enhance our comprehension of N 2 O emissions at various interfaces of cascade reservoirs but also offer theoretical backing for the formulation of management strategies aimed at efficiently mitigating N 2 O emissions from continuously dammed rivers., 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: Limited attention has been paid to the health effects of long-term PM 1 exposure on stroke admission. Current investigations exploring the long-term PM exposure effect are largely based on observational studies, and PM generally is not allocated randomly to participants. Using traditional regression models might confuse messaging and hinder policy recommendations for pollution control and disease prevention policies., Methods: We conducted a cohort study among 36,271 adults from one of the largest cities in China in 2015 and followed up through 2020. Hazard ratios of stroke admissions following long-term PM 1 exposure were estimated via a causal inference approach, marginal structural time-varying Cox proportional hazard model, accounting for multiple confounders. Additionally, several sensitivity analyses and impact modification analyses were carried out., Results and Discussion: Associations with 1 μg/m 3 increase in long-term PM 1 were identified for total (HR, 1.079; 95 %CI, 1.012-1.151) and ischemic stroke admissions (HR, 1.092; 95 %CI, 1.018-1.171). The harmful associations varied with exposure duration, initially increasing and then decreasing. The 2-3 years cumulative exposure was associated with a 3.3-5.4 % raised risk for total stroke. For every 1 μg/m³ increase in long-term PM 1 exposure, females exhibited a higher risk of both total and ischemic stroke (13 % and 16 %) than men (4 % and 5 %). Low-exposure individuals (whose annual PM 1 concentrations were under the third quartile among the annual concentrations for all the participants) exhibited greater sensitivity to PM 1 effects (total stroke: 1.079 vs. 1.107; ischemic stroke: 1.092 vs. 1.116). The results underline the importance of safeguarding low-exposed people in highly polluted areas and suggest that long-term PM 1 exposure may increase stroke admission risk, warranting attention to vulnerable groups., 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 Inc.)