Air Pollution Control Strategies Research Articles

Life Course Associations Between Ambient Fine Particulate Matter and the Prevalence of Prediabetes and Diabetes: A Longitudinal Cohort Study in Taiwan and Hong Kong.

Both air pollution and diabetes are key urban challenges. The association between particulate matter with a diameter of <2.5 μm (PM2.5) exposure and prediabetes/diabetes in adults is well documented, but the health effects of life course exposure remain unclear. This study evaluated the impact of PM2.5 exposure throughout various life stages on the prevalence of prediabetes/diabetes in adulthood. We included 4,551 individuals with 19,593 medical visits from two open cohorts in Taiwan and Hong Kong between 2000 and 2018. Ambient PM2.5 exposure was assessed using a satellite-based model, delivering a 2-year average exposure at a resolution of 1 km2. Logistic mixed-effects models were used to investigate longitudinal associations between PM2.5 exposure and the prevalence of prediabetes/diabetes. Life course models were used to examine the impact of PM2.5 exposure at different life stages on prediabetes/diabetes in adulthood. Over an average follow-up period of 9.93 years, 1,660 individuals with prediabetes/diabetes were observed. For the longitudinal association, every 10 μg/m3 increase in PM2.5 was associated with an increased odds of having prediabetes/diabetes (odds ratio 1.32, 95% CI 1.13, 1.54). The odds of adulthood prediabetes/diabetes increased by 15%, 18%, and 29% for each 10 μg/m3 increase in PM2.5 exposure during school age, adolescence, and adulthood, respectively. Our findings suggest a link between PM2.5 exposure during each life stage and the prevalence of prediabetes/diabetes in adulthood, with the health impacts of exposure during adulthood being slightly greater. This study underscores the need for life course air pollution control strategies to mitigate the substantial disease burden of diabetes.

Prediction of Hydrogen Production from Solid Oxide Electrolytic Cells Based on ANN and SVM Machine Learning Methods

In recent years, the application of machine learning methods has become increasingly common in atmospheric science, particularly in modeling and predicting processes that impact air quality. This study focuses on predicting hydrogen production from solid oxide electrolytic cells (SOECs), a technology with significant potential for reducing greenhouse gas emissions and improving air quality. We developed two models using artificial neural networks (ANNs) and support vector machine (SVM) to predict hydrogen production. The input variables are current, voltage, communication delay time, and real-time measured hydrogen production, while the output variable is hydrogen production at the next sampling time. Both models address the critical issue of production hysteresis. Using 50 h of SOEC system data, we evaluated the effectiveness of the ANN and SVM methods, incorporating hydrogen production time as an input variable. The results show that the ANN model is superior to the SVM model in terms of hydrogen production prediction performance. Specifically, the ANN model shows strong predictive performance at a communication delay time ε = 0.01–0.02 h, with RMSE = 2.59 × 10−2, MAPE = 33.34 × 10−2%, MAE = 1.70 × 10−2 Nm3/h, and R2 = 99.76 × 10−2. At delay time ε = 0.03 h, the ANN model yields RMSE = 2.74 × 10−2 Nm3/h, MAPE = 34.43 × 10−2%, MAE = 1.73 × 10−2 Nm3/h, and R2 = 99.73 × 10−2. Using the SVM model, the prediction error values at delay time ε = 0.01–0.02 h are RMSE = 2.70 × 10−2 Nm3/h, MAPE = 44.01 × 10−2%, MAE = 2.24 × 10−2 Nm3/h, and R2 = 99.74 × 10−2, while at delay time ε = 0.03 h they become RMSE = 2.67 × 10−2 Nm3/h, MAPE = 43.44 × 10−2%, MAE = 2.11 × 10−2 Nm3/h, and R2 = 99.75 × 10−2. With this precision, the ANN model for SOEC hydrogen production prediction has positive implications for air pollution control strategies and the development of cleaner energy technologies, contributing to overall improvements in air quality and the reduction of atmospheric pollutants.

Chemical Profiles of Particulate Matter Emitted from Anthropogenic Sources in Selected Regions of China

Particulate matter (PM) emissions from anthropogenic sources contribute substantially to air pollution. The unequal adverse health effects caused by source-emitted PM emphasize the need to consider the discrepancy of PM-bound chemicals rather than solely focusing on the mass concentration of PM when making air pollution control strategies. Here, we present a dataset about chemical compositions of real-world PM emissions from typical anthropogenic sources in China, including industrial (power, industrial boiler, iron & steel, cement, and other industrial process), residential (coal/biomass burning, and cooking), and transportation sectors (on-road vehicle, ship, and non-exhaust emission). The data was obtained under the same strict quality control condition on field measurements and chemical analysis, minimizing the uncertainty caused by different study approaches. The concentrations of PM-bound chemical components, including toxic elements and PAHs, exhibit substantial discrepancies among different emission sectors. This dataset provides experimental data with informative inputs to emission inventories, air quality simulation models, and health risk estimation. The obtained results can gain insight into understanding on source-specific PMs and tailoring effective control strategies.

Quantitative Decoupling Analysis for Assessing the Meteorological, Emission, and Chemical Influences on Fine Particle Pollution

AbstractA comprehensive understanding of meteorological, emission and chemical influences on severe haze is essential for air pollution mitigation. However, the nonlinearity of the atmospheric system greatly hinders this understanding. In this study, we developed the quantitative decoupling analysis (QDA) method by applying the Factor Separation (FS) method into the model processes to quantify the effects of emissions (E), meteorology (M), chemical reactions (C), and their nonlinear interactions and impact on fine particulate matter (PM2.5) pollution. Taking a heavy‐haze episode in Beijing as an example, we show that different from the integrated process rate (IPR) and the scenario analysis approach (SAA) in previous studies, the QDA method explicitly demonstrate the nonlinear effects by decomposing the variation of PM2.5 concentration into individual contributions of E, M and C terms as well as the contributions from interactions among these processes. Results showed that M dominated the hourly fluctuation of the PM2.5 concentration. The C terms increase with increasing the level of haze, reaching maximum (0.37 μg m−3 h−1) at the maintenance stage. Moreover, our method reveals that there are non‐negligible non‐linear effects of meteorological, emission, and chemical processes during pollution stage, with the mean accounting for 50% of the increase in PM2.5 concentrations, which is often ignored in the current air pollution control strategies. This study highlights that the QDA approach can be used to gain insight into the formation of heavy pollution, and to identify uncertainty in numerical models.

Air quality in different urban hotspots in a metropolitan city in India and the environmental implication.

This research study investigates hourly data on concentrations of five major air pollutants such as particulate matter (PM10, PM2.5) and gaseous pollutants (SO2, NO2, CO) measured during 2022 at four hotspot sites (industrial site, traffic site, commercial site, harbour, and one residential site) in Chennai, India. The analysis encompasses temporal variations spanning annual, seasonal, and diurnal variations in the pollutants. Notably, PM10 and CO emerge as the predominant pollutants, with the highest concentrations at industrial and traffic sites (PM10: 67.64 ± 40.77µg/m3, CO: 1.41 ± 0.84mg/m3; traffic site: PM10: 58.67 ± 20.05µg/m3, CO: 0.99 ± 0.57mg/m3). Seasonal dynamics reveal prominent winter spikes in particulate matter (PM10, PM2.5) and carbon monoxide (CO) concentrations, while nitrogen dioxide (NO2) and sulphur dioxide (SO2) levels peak during the summer season, particularly in the harbour area. The proximity to roadways exerts a discernible influence on diurnal patterns, with traffic sites showcasing broader rush hour peaks compared to sharper spikes observed at other sites. Furthermore, distinct bimodal patterns are evident for PM10 and PM2.5 concentrations in residential and harbour areas. A common lognormal distribution pattern is identified across the studied sites, suggesting consistent air quality trends despite contrasting locations. The conditional probability function (CPF) is used in conjunction with local meteorological conditions for identifying key pollution sources in each location. The implementation of polar plots emphasizes industries as principal local sources of pollution, at industrial sites significantly contributing to PM10, SO2, and NO2 concentrations under specific wind conditions. The main objective of the present study is to facilitate a good understanding of pollutant dynamics, pollution sources, and their intricate interplay with meteorological factors, thereby contributing to the formulation and implementation of effective air pollution control and mitigation strategies.

Estimates of Air Pollutant Emissions by Vehicle Fleet in the State of Pará, Brazil: Impacts and Trends

Objective: aims to estimate emissions of the main atmospheric pollutants from vehicular sources in the state of Pará, to continue the environmental agenda of monitoring air quality. Theoretical Framework: The growing global concern about air quality in urban areas is driven by the environmental and public health impacts caused by air pollution. The concentration of pollutants from vehicle sources represents one of the main challenges, especially in densely populated regions such as the capital and other major cities in the state of Pará, where the automotive fleet has increased significantly. Method: The methodology used combined "bottom-up" and "top-down" methods to calculate emissions of carbon monoxide (CO), nitrogen oxides (NO) and total hydrocarbons (HC) for different vehicle categories and fuel types. Data on licensed vehicle fleets and fuel consumption obtained from DETRAN-PA and ANP were used. Results and Discussion: The results revealed that gasoline-powered vehicles are responsible for the majority of CO and HC emissions, while diesel vehicles emit the largest amounts of NO into the atmosphere. Motorcycles were identified as the largest source of emissions due to their larger number in the circulating fleet. Our results also highlight the importance of assessing and monitoring vehicle emissions to develop effective air pollution control strategies. Research Implications: The information obtained in this study is unprecedented for the study area, and can therefore support the formulation of environmental policies aimed at reducing emissions of atmospheric pollutants in the state of Pará, thus promoting an improvement in air quality and public health. Originality/Value: This paper makes an unprecedented contribution to national and international literature on environmental pollution in large urban centers. On the eve of the largest climate and environment conference on the planet (COP30) to be held in Pará, Amazonas, this research joins efforts in relation to the monitoring, forecasting and mitigation of air pollution, considered one of the main environmental problems of today.

Dynamics of atmospheric emissions and meteorological variables in Bangladesh from pre-to post-COVID-19 lockdown

Following the COVID-19 restrictions, there was a sharp decline in global air quality and related environmental metrics. Due to the limited availability of in situ atmospheric data in Bangladesh, this study collected data on various air pollutants (NO2, SO2, CO, and PM2.5), greenhouse gases (CO2, CH4, and O3), as well as meteorological variables like Land Surface Temperature (LST), Relative Humidity (RH), Precipitation, surface albedo and Aerosol Optical Depth (AOD) from different datasets by Google Earth Engine (GEE), the International Energy Agency (IEA), NASA Giovanni, and NASA Power Access Viewer, covering periods before (2019), during (2020), and after (2021–2023) the COVID-19 lockdown in Bangladesh. GIS-based assessment alongside Principal Component Analysis (PCA) has been performed to explore the patterns, trends and correlations among the observed variables. Results showed in 2020 compared to 2019, NO2, SO2, CO, PM2.5, and CO2 concentrations decreases by 1.94, 16.67, 1.95, 2.08, and 6 %, respectively, while CH4 and O3 continued to rise. Meteorological variables exhibited a 0.16 °C decreases in LST, 6.4 % increases in RH, a 6 % reduction in AOD, and 6.36 % declines in surface albedo. Post-lockdown in 2021, air pollutants surged (NO2, SO2, CO, and PM2.5 increases by 17.3, 23.6, 0.6, and 8.3 %, respectively), with CO2, LST, and AOD rising by 8.5 %, 0.13 °C, and 8.3 %, and a slight 0.46 % decrease in RH compared to 2019 due to resuming more economic activities, transportation and industrial production works. The years 2022–2023 saw slight improvements in most variables except CH4, though concentrations did not revert to those of 2019. The findings of correlation coefficients revealed that pollutants and GHG are highly correlated with the meteorological variables specially with RH. This study underscores the substantial shifts in atmospheric variables from pre-to post-lockdown periods, offering valuable insights for more effective management of the greenhouse effect and air pollution control strategies.

Comparison of aerosol number size distribution and new particle formation in summer at alpine and urban regions in the Guanzhong Plain, Northwest China

Ultrafine particles play a crucial role in understanding climate change, mitigating adverse health effects, and developing strategies for air pollution control. However, the factors influencing the occurrence and development of new particle formation (NPF) events, as well as the underlying chemical mechanisms, remain inadequately explained. This study compared number concentrations and size distributions of atmospheric ultrafine particles at Xi'an (urban area) and the summit of Mt. Hua (alpine region) in summer to investigate the NPF mechanism and particle growth in both clean and polluted areas of the Guanzhong Plain. The average particle number concentration in Xi'an was significantly higher than that at Mt. Hua. The diurnal variation of total particle number concentration differed between Xi'an and Mt. Hua indicating a divergence in influencing factors. The size distributions in Xi'an varied across different timescales and weather conditions, whereas Mt. Hua exhibited little variation. This stability at Mt. Hua is attributed to its cleaner background atmosphere and the steady influx of aging particles with larger diameters transported from the free atmosphere. In both areas, geometric mean diameters (GMDs) were inversely proportional to particle number concentrations suggesting that increase in particle numbers were primarily due to the generation of smaller particles. The potential governing factors for NPF events differed somewhat between the urban and mountainous stations. In the urban area, intense local stationary and mobile emission sources promoted the growth of newly formed nanoparticles, with ozone-oxidized condensable vapors serving as key precursors. In contrast, at the mountainous station, NPF process were significantly influenced by anthropogenic precursors from long-range transport and locally emitted biogenic organics. The rapid increase in ultrafine particle concentrations primarily poses serious health risks and degrades air quality in urban areas, while also contributing to climate-related effects in alpine regions.

Research on Coal Mine Environmental Pollution Management and Ecological Restoration Technology

This paper analyses the current situation of environmental pollution in coal mines, its causes, and its impact on the ecological environment, and systematically researches the management of environmental pollution in coal mines and ecological restoration technology. The paper analyses the main categories of coal mine environmental pollution, including water pollution, air pollution, and solid waste pollution, and discusses the causes of these pollution and their destructive effects on the ecological environment. Immediately after that, the article elaborates on the treatment technologies of coal mine environmental pollution, including physical, chemical and biological treatment technologies for water pollution control, blocking, adsorption and covering technologies for air pollution control, and landfill, incineration and resource utilization strategies for solid waste disposal. This paper also discusses coal mine ecological restoration techniques, including species selection and allocation for vegetation restoration, vegetation restoration models, physical, chemical and biological methods for soil improvement, and engineering, vegetation and agricultural measures for soil and water conservation. Through the research of this paper, the researchers aim to provide scientific basis and technical support for the management of coal mine environmental pollution and ecological restoration and to promote the restoration of the ecological environment in mining areas and the sustainable development of the coal industry.

Vertical structure and transport characteristic of aerosol and O3 during the emergency control period in Wuhan, China, using vehicle-lidar observations

During the Lunar New Year of 2020, the Chinese government implemented a strict nationwide lockdown to prevent the spread of the COVID-19, which may have led to changes in the distribution of pollutants. To enhance our understanding of the three-dimensional evolution of pollutants under special control scenarios, this study used vehicle lidar to obtain vertical profiles of aerosols and ozone (O3) in Wuhan during the pandemic. Combined with ground observations and WRF-Chem model simulation results, we explored the evolution characteristics of pollutants. The results showed that the fine particulate matter (PM2.5) concentration near the ground in Wuhan City was significantly reduced (40%) during the emission control period. The aerosol concentration decreased rapidly with an increase in height, and the maximum loading height was consistent with the atmospheric boundary layer height. Conversely, due to factors such as the reduction of the precursor nitrogen dioxide (NO2) and rising temperatures, the near-ground O3 concentration increased by 59%. Despite the significant reduction in emission sources, lidar observations still captured distinct aerosol transport layers on March 7 and March 10, 2020. These transport layers gradually descended, affecting the ground-level aerosol concentration. Additionally, one typical process of O3 external transport was observed in the vertical direction. Our study provides a theoretical basis for a deeper understanding of the spatiotemporal distribution characteristics of pollutants during special control scenarios, aiding policymakers in formulating effective air pollution control strategies.

Assessing the nonlinearity of wintertime PM2.5 formation in response to precursor emission changes in North China with the adjoint method

Abstract While China’s clean air actions implemented since 2013 have been effective in mitigating PM2.5 air pollution, the large emission reductions during the COVID-19 lockdown period in early 2020 did not similarly alleviate PM2.5 pollution in North China, reflecting a distinct nonlinear chemical response of PM2.5 formation to emission changes. Here we apply emission-concentration relationships for PM2.5 diagnosed using the adjoint approach to quantitatively assess how chemical nonlinearity affects PM2.5 over Beijing in February 2020 in response to two emission reduction scenarios: the COVID-19 lockdown and 2013–2017 emission controls. We find that, in the absence of chemical nonlinearity, the COVID-19 lockdown would decrease PM2.5 in Beijing by 17.9 μg m–3, and the 2013–2017 emission controls resulted in a larger decrease of 54.2 μg m–3 because of greater reductions of SO2 and primary aerosol emissions. Chemical nonlinearity offset the decrease for Beijing PM2.5 by 3.4 μg m–3 during the lockdown due to enhanced sensitivity of aerosol nitrate to NO x emissions, but enhanced the efficiency of 2013–2017 emission controls by 11.9 μg m–3 due to the weakened heterogeneous reaction of sulfate. Such nonlinear chemical effects are important to estimate and consider when designing or assessing air pollution control strategies.

Pemodelan Spasial Pengaruh Ruang Terbuka Hijau (RTH) Dengan Mengukur Tingkat Kadar Gas (Karbon Monoksida) (Studi Kasus: : Kelurahan Entrop Distrik Jayapura Selatan)

This research aims to model the impact of Green Open Space (GOS) on Carbon Monoxide (CO) gas levels in Entrop Village, South Jayapura District. Green Open Space plays a crucial role in improving air quality in an area. In the context of rapid urbanization and increasing motor vehicle numbers, GOS serves as the city's lungs, capable of absorbing pollutants and improving air quality. The research method used is spatial modeling, which allows mapping the distribution of GOS and measuring CO levels on the main roads of Entrop Village. Primary data was obtained through field surveys using CO measuring instruments, while secondary data was obtained from various literature sources and reports from related agencies. Data analysis was conducted using statistical and spatial approaches to identify the relationship between GOS and CO levels. The results showed a significant negative relationship between the extent of GOS and CO levels. The larger the GOS area, the lower the CO concentration in the area. Spatial modeling also identified high CO concentration points in areas with low GOS density. Furthermore, this research found that uneven distribution of GOS contributes to variations in air quality (CO) in different locations. These findings highlight the importance of effective GOS management as part of air pollution control strategies in large cities. Recommendations from this research include increasing the number and distribution of GOS in areas with high CO concentrations, and integrating GOS into urban spatial planning to create a healthier and more sustainable environment. This research makes a significant contribution to sustainable urban planning by emphasizing the importance of integrating GOS into spatial planning to improve air quality and environmental health. Thus, the results of this research can serve as a reference for the government and stakeholders in formulating future GOS management and air pollution control policies.

Optimizing air quality and health Co-benefits of mitigation technologies in China: An integrated assessment

Carbon mitigation technologies lead to air quality improvement and health co-benefits, while the practical effects of the technologies are dependent on the energy composition, technological advancements, and economic development. In China, mitigation technologies such as end-of-pipe treatment, renewable energy adoption, carbon capture and storage (CCS), and sector electrification demonstrate significant promise in meeting carbon reduction targets. However, the optimization of these technologies for maximum co-benefits remains unclear. Here, we employ an integrated assessment model (AIM/enduse, CAM-chem, IMED|HEL) to analyze air quality shifts and their corresponding health and economic impacts at the provincial level in China within the two-degree target. Our findings reveal that a combination of end-of-pipe technology, renewable energy utilization, and electrification yields the most promising results in air quality improvement, with a reduction of fine particulate matter (PM2.5) by −34.6 μg m−3 and ozone by −18.3 ppb in 2050 compared to the reference scenario. In contrast, CCS technology demonstrates comparatively modest improvements in air quality (−9.4 μg m−3 for PM2.5 and −2.4 ppb for ozone) and cumulative premature deaths reduction (−3.4 million from 2010 to 2050) compared to the end-of-pipe scenario. Notably, densely populated regions such as Henan, Hebei, Shandong, and Sichuan experience the most health and economic benefits. This study aims to project effective future mitigation technologies and climate policies on air quality improvement and carbon mitigation. Furthermore, it seeks to delineate detailed provincial-level air pollution control strategies, offering valuable guidance for policymakers and stakeholders in pursuing sustainable and health-conscious environmental management.

Global health costs of ambient PM2·5 from combustion sources: a modelling study supporting air pollution control strategies

Climate actions targeting combustion sources can generate large ancillary health benefits via associated air-quality improvements. Therefore, understanding the health costs associated with ambient fine particulate matter (PM2·5) from combustion sources can guide policy design for both air pollution and climate mitigation efforts. In this modelling study, we estimated the health costs attributable to ambient PM2·5 from six major combustion sources across 204 countries using updated concentration-response models and an age-adjusted valuation method. We defined major combustion sources as the sum of total coal, liquid fuel and natural gas, solid biofuel, agricultural waste burning, other fires, and 50% of the anthropogenic fugitive, combustion, and industrial dust source. Global long-term exposure to ambient PM2·5 from combustion sources imposed US$1·1 (95% uncertainty interval 0·8-1·5) trillion in health costs in 2019, accounting for 56% of the total health costs from all PM2·5 sources. Comparing source contributions to PM2·5 concentrations and health costs, we observed a higher share of health costs from combustion sources compared to their contribution to population-weighted PM2·5 concentration across 134 countries, accounting for more than 87% of the global population. This disparity was primarily attributed to the non-linear relationship between PM2·5 concentration and its associated health costs. Globally, phasing out fossil fuels can generate 23% higher relative health benefits compared to their share of PM2·5 reductions. Specifically, the share of health costs for total coal was 36% higher than the source's contributions to corresponding PM2·5 concentrations and the share of health costs for liquid fuel and natural gas was 12% higher. Other than fossil fuels, South Asia was expected to show 16% greater relative health benefits than the percentage reduction in PM2·5 from the abatement of solid biofuel emissions. In most countries, targeting combustion sources might offer greater health benefits than non-combustion sources. This finding provides additional rationale for climate actions aimed at phasing out combustion sources, especially those related to fossil fuels and solid biofuel. Mitigation efforts designed according to source-specific health costs can more effectively avoid health costs than strategies that depend solely on the source contributions to overall PM2·5 concentration. The Health Effects Institute, the National Natural Science Foundation of China, and NASA.

Measurement of volatile organic compounds using tethered balloons in a polluted industrial site in Catalonia (Spain).

Understanding the chemical composition of volatile organic compounds (VOCs) near emission sources and in the background atmosphere above the mixing layer height (MLH) provides insight into the fate of VOCs and is essential for developing effective air pollution control strategies. Unfortunately, knowledge of the qualitative and quantitative changes of VOCs and their vertical transport in the atmosphere is limited due to challenging experimental setups. In this study, an innovative method using tethered balloons was tested and implemented to sample 40 VOCs and O3 below and above the MLH at an industrial site in Spain. VOC and O3 samples were collected with different types of sorbent cartridges and analyzed using chromatographic techniques. Overall, a decrease in VOC concentration with altitude was observed along with a homogeneous chemical composition up to 300 m AGL. This decrease with altitude denoted the primary origin of these VOCs, which were strongly influenced by industrial processes and the traffic emissions in the area. Conversely, O3 concentrations were notably higher at balloon level and increased during nighttime temperature inversion episodes in those samples collected above the mixing layer. Ground samples contained freshly emitted pollutants of industrial origin, while balloon samples consisted of aged pollutants from traffic, other combustion sources, or from a secondary origin. This study is the first to assess the vertical composition of VOCs at a site of these characteristics and demonstrates that tethered balloons are a cost-effective method for studying air pollution dynamics from the ground to higher altitudes in the low troposphere.

Diel variations of airborne microbes and antibiotic resistance genes in Response to urban PM2.5 chemical properties during the heating season

Among the components of fine particulate matter (PM2.5), the contributions of airborne microorganisms and antibiotic resistance genes (ARGs) to health risks have been overlooked. Airborne microbial dynamics exhibit a unique diurnal cycle due to environmental influences. However, the specific roles of PM2.5 chemical properties resulting from fossil fuel combustion in driving circadian fluctuations in microbial populations and ARGs remain unclear. This study explored the interactions between toxic components and microbial communities during the heating period to understand the variations in ARGs. Bacterial and fungal communities showed a higher susceptibility to diel variations in PM2.5 compared to their chemical properties. Mantel tests revealed that chemical properties and microbial community interactions contribute differently to ARG variations, both directly and indirectly, during circadian fluctuations. Our findings highlight that, during the daytime, the enrichment of pathogenic microorganisms and ARGs increases the risk of PM2.5 toxicity. Conversely, during the nighttime, the utilization of water-soluble ions by the fungal community increased, leading to a significant increase in fungal biomass. Notably, Aspergillus exhibited a significant correlation with mobile genetic elements and ARGs, implying that this genus is a crucial driver of airborne ARGs. This study provides novel insights into the interplay between the chemical composition, microbial communities, and ARGs in PM, underscoring the urgent need for a comprehensive understanding of effective air pollution control strategies.

Transfer learning in environmental data-driven models: A study of ozone forecast in the Alpine region

Many environmental variables, in particular, related to air or water quality, are measured in a limited number of points and often for a limited time span. This forbids the development of accurate models for interesting locations with missing or insufficient data and poses the question of whether a model developed for another measurement site can be reliably applied. Such a question is particularly critical when the model is entirely data-driven, such as a neural network. In this context, the paper proposes a procedure to evaluate the expected performance of an existing neural network model applied to a new unmonitored station. This transferability assessment is exemplified by the problem of forecasting ozone concentrations in different environmental settings around the Alpine Arc. Long Short-Term Memory (LSTM) neural network models are applied for predicting hourly concentrations in 20 stations of different types (urban, rural, and mountain). The analysis of the results allows us to determine the expected performance of such models in new cases and reduce the transferability uncertainty when the existing models can be partitioned into clusters. The LSTM models demonstrate the possibility of high accuracy in ozone forecasting at all sites. Given the significant impacts of this gas on human health and the environment, this can contribute to better decision-making and mitigation strategies for air pollution control.

Challenges and perspectives of air pollution control in China

Air pollution is one of the most challenging environmental issues in the world. China has achieved remarkable success in improving air quality in last decade as a result of aggressive air pollution control policies. However, the average fine particulate matter (PM2.5) concentration in China is still about six times of the World Health Organization (WHO) Global Air Quality Guidelines (AQG) and causing significant human health risks. Extreme emission reductions of multiple air pollutants are required for China to achieve the AQG. Here we identify the major challenges in future air quality improvement and propose corresponding control strategies. The main challenges include the persistently high health risk attributed to PM2.5 pollution, the excessively loose air quality standards, and coordinated control of air pollution, greenhouse gases (GHGs) emissions and emerging pollutants. To further improve air quality and protect human health, a health-oriented air pollution control strategy shall be implemented by tightening the air quality standards as well as optimizing emission reduction pathways based on the health risks of various sources. In the meantime, an “one-atmosphere” concept shall be adopted to strengthen the synergistic control of air pollutants and GHGs and the control of non-combustion sources and emerging pollutants shall be enhanced.

Long-term exposure to fine particulate matter constituents in relation to chronic kidney disease: evidence from a large population-based study in China.

The effects of long-term exposure to fine particulate matter (PM2.5) constituents on chronic kidney disease (CKD) are not fully known. This study sought to examine the association between long-term exposure to major PM2.5 constituents and CKD and look for potential constituents contributing substantially to CKD. This study included 81,137 adults from the 2018 to 2019 baseline survey of China Multi-Ethnic Cohort. CKD was defined by the estimated glomerular filtration rate. Exposure concentration data of 7 major PM2.5 constituents were assessed by satellite remote sensing. Logistic regression models were used to estimate the effect of each PM2.5 constituent exposure on CKD. The weighted quantile sum regression was used to estimate the effect of mixed exposure to all constituents. PM2.5 constituents had positive correlations with CKD (per standard deviation increase), with ORs (95% CIs) of 1.20 (1.02-1.41) for black carbon, 1.27 (1.07-1.51) for ammonium, 1.29 (1.08-1.55) for nitrate, 1.20 (1.01-1.43) for organic matter, 1.25 (1.06-1.46) for sulfate, 1.30 (1.11-1.54) for soil particles, and 1.63 (1.39-1.91) for sea salt. Mixed exposure to all constituents was positively associated with CKD (1.68, 1.32-2.11). Sea salt was the constituent with the largest weight (0.36), which suggested its importance in the PM2.5-CKD association, followed by nitrate (0.32), organic matter (0.18), soil particles (0.10), ammonium (0.03), BC (0.01). Sulfate had the least weight (< 0.01). Long-term exposure to PM2.5 sea salt and nitrate may contribute more than other constituents in increasing CKD risk, providing new evidence and insights for PM2.5-CKD mechanism research and air pollution control strategy.

Weakened aerosol–radiation interaction exacerbating ozone pollution in eastern China since China's clean air actions

Abstract. Since China's clean air action, PM2.5 (particulate matter with an aerodynamic equivalent diameter of 2.5 µm or less) air quality has improved, while ozone (O3) pollution has become more severe. Here we apply a coupled meteorology–chemistry model (WRF-Chem: Weather Research and Forecasting model coupled to Chemistry v3.7.1) to quantify the responses of aerosol–radiation interaction (ARI) to anthropogenic emission reductions from 2013 to 2017, including aerosol–photolysis interaction (API) related to photolysis rate change and aerosol–radiation feedback (ARF) related to meteorological field change and their contributions to O3 increases over eastern China in summer and winter. Sensitivity experiments show that the decreased anthropogenic emissions play a more prominent role in the increased daily maximum 8 h average (MDA8) O3 in both summer (+1.96 ppb vs. +0.07 ppb) and winter (+3.56 ppb vs. −1.08 ppb) than the impacts of changed meteorological conditions in urban areas. The decreased PM2.5 caused by emission reductions can result in a weaker impact of ARI on O3 concentrations, which superimposes its effect on the worsened O3 air quality. The weakened ARI due to decreased anthropogenic emissions aggravates the summer (winter) O3 pollution by +0.81 ppb (+0.63 ppb), averaged over eastern China, with weakened API contributing 55.6 % (61.9 %) and ARF contributing 44.4 % (38.1 %), respectively. This superimposed effect is more significant for urban areas during summer (+1.77 ppb). Process analysis indicates that the enhanced chemical production is the dominant process for the increased O3 concentrations caused by weakened ARI in both summer and winter. This study innovatively reveals the adverse effect of weakened aerosol–radiation interaction due to decreased anthropogenic emissions on O3 air quality, indicating that more stringent coordinated air pollution control strategies should be implemented for significant improvements in future air quality.