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The rapid urbanization of urban areas in China has brought about great variation in the layout of cities and serious air pollution. Recently, the focus has been directed toward understanding the role of urban morphology in the generation and spread of atmospheric pollution, particularly in PM2.5 emissions. However, there have been limited investigations into the impact of three-dimensional (3D) features on changes in PM2.5 concentrations. By analyzing a wealth of data on building structures based on a mixed linear model and variance partition analysis in the Yangtze River Delta throughout 2018, this study sought to examine the associations between PM2.5 concentrations and urban building form, and further compared the contributions of two-dimensional (2D) and 3D building features. The findings revealed that both 2D and 3D building forms played an important role in PM2.5 concentrations. Notably, the greater contribution of 3D building forms on PM2.5 concentrations was observed, especially during the summer, where they accounted for 20% compared to 7% for 2D forms. In particular, the building height range emerged as a crucial local factor affecting PM2.5 concentrations, contributing up to 12%. Moreover, taller buildings with more variability in height were found to aid in the dispersion of pollution. These results underscore the substantial contribution of 3D building morphology to PM2.5 pollution, contrasting with previous studies. Furthermore, compact buildings were linked to lower pollution levels, and an urban landscape characterized by polycentric urban structures and lower fragmentation was deemed more favorable for sustainable urban development. This study is significant in investigating the contribution of 3D morphology to PM2.5 and its importance for pollution dispersion mechanisms. It suggests the adoption of a polycentric urban form with a broader range of building heights in urban planning for local governments in the Yangtze River Delta. [ABSTRACT FROM AUTHOR]

This study investigates the spatial and temporal variations of PM2.5 concentrations in Delhi from 2014 to 2023, utilizing ground-based measurements from the Central Pollution Control Board (CPCB) and MERRA-2 reanalysis data. The analysis reveals strong positive correlations (r > 0.90) across all districts, highlighting city-wide factors influencing PM2.5 levels, such as vehicular emissions, industrial activities, and regional weather patterns. Seasonal patterns show PM2.5 concentrations peaking during winter, attributed to lower temperatures, reduced wind speeds, and increased emissions from heating sources. To enhance the accuracy of PM2.5 predictions, various machine learning (ML) models were employed, including Extra Trees Regressor, Random Forest Regressor, Light Gradient Boosting Machine (LGBM) Regressor, and a Stacking Regressor. These models utilized MERRA-2 sub-parameters like Dust, Organic Carbon, Black Carbon, Sea Salt, and Sulfate. The Stacking Regressor demonstrated the best performance, achieving an R² value of 0.67 and a significant improvement in correlation with CPCB measurements (r = 0.86). The ML models significantly improved the prediction accuracy of PM2.5 concentrations compared to the original MERRA-2 data, reducing the Mean Bias from -39.4 µg/m³ to around 10.4µg/m³ and the Root Mean Squared Error (RMSE) from 71.1 µg/m³ to below 40 µg/m³. Additionally, the Fraction of predictions within a factor of 2 increased from 0.61 for MERRA-2 to over 0.89 for all ML models. These findings underscore the effectiveness of integrating machine learning models with MERRA-2 sub-parameters to accurately estimate PM2.5 concentrations. This approach provides more reliable predictions of air quality, essential for developing targeted and effective air quality management strategies in Delhi. [ABSTRACT FROM AUTHOR]

The negative trend of PM2.5 in southwestern Taiwan could be offset in the future because of the stabilisation of the atmospheric layer with the decreasing strength of the East Asian winter monsoons. However, phenomena such as changes in the moisture source and the washout effect can help reduce PM2.5 levels. To assess these phenomena, hourly PM2.5 levels, weather parameters, and surface weather maps were obtained from the relevant government authorities in Taiwan. Trend, multiregression, and basic statistical methods were used in this study. The study period was divided into two separate parts. P1 (1959–1976) represents the averaged period, and P2 represents the warming period (1977–2021). The rainy-day weather patterns suggested that the percentage of moisture in the weak continental high area south of the Yangtze River basin in China and the southern region outside Taiwan Island was more substantial during P2 (28.3%) than during P1 (7.3%) because the frequency and intensity of rainfall increased in P2. The rainfall pattern in the Kaoping area from 2005 to 2021 changed from low rainfall and decreasing rainfall frequency to high rainfall and increasing rainfall frequency. Multiregression models showed that the negative contribution of the washout effect to the variation in the annual mean PM2.5 was more significant than the positive contribution of air dispersion. This suggests that in tropical regions with poor air dispersion and winter monsoons, the frequency and type of rainfall affect the variations in PM2.5 under climate change. [ABSTRACT FROM AUTHOR]

Although all walks of life are paying less attention to COVID-19, the spread of COVID-19 has never stopped. As an infectious disease, its transmission speed is closely related to the atmosphere environment, particularly the temperature (T) and PM2.5 concentrations. However, How T and PM2.5 concentrations are related to the spread of SARS-CoV-2 and how much their cumulative lag effect differ across cities is unclear. To identify the characteristics of cumulative lag effects of environmental exposure under city differences, this study used a generalized additive model to investigate the associations between T/PM2.5 concentrations and the daily number of new confirmed COVID-19 cases (NNCC) during the outbreak period in the second half of 2021 in Shaoxing, Shijiazhuang, and Dalian. The results showed that except for PM2.5 concentrations in Shaoxing, the NNCC in the three cities generally increased with the unit increase of T and PM2.5 concentrations. In addition, the cumulative lag effects of T/PM2.5 concentrations on NNCC in the three cities reached a peak at lag 26/25, lag 10/26, and lag 18/13 days, respectively, indicating that the response of NNCC to T and PM2.5 concentrations varies among different regions. Therefore, combining local meteorological and air quality conditions to adopt responsive measures is an important way to prevent and control the spread of SARS-CoV-2.

Although all walks of life are paying less attention to COVID-19, the spread of COVID-19 has never stopped. As an infectious disease, its transmission speed is closely related to the atmosphere environment, particularly the temperature (T) and PM 2.5 concentrations. However, How T and PM 2.5 concentrations are related to the spread of SARS-CoV-2 and how much their cumulative lag effect differ across cities is unclear. To identify the characteristics of cumulative lag effects of environmental exposure under city differences, this study used a generalized additive model to investigate the associations between T/PM 2.5 concentrations and the daily number of new confirmed COVID-19 cases (NNCC) during the outbreak period in the second half of 2021 in Shaoxing, Shijiazhuang, and Dalian. The results showed that except for PM 2.5 concentrations in Shaoxing, the NNCC in the three cities generally increased with the unit increase of T and PM 2.5 concentrations. In addition, the cumulative lag effects of T/PM 2.5 concentrations on NNCC in the three cities reached a peak at lag 26/25, lag 10/26, and lag 18/13 days, respectively, indicating that the response of NNCC to T and PM 2.5 concentrations varies among different regions. Therefore, combining local meteorological and air quality conditions to adopt responsive measures is an important way to prevent and control the spread of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Owing to uneven environmental monitoring site distribution, there are significant spatial data gaps for concentrations of ambient fine particles with diameters ≤ 2.5 µm (PM2.5) obtained using traditional monitoring methods. Satellite products are an alternative data source for locations where monitoring sites are unavailable. The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product has been widely used in PM2.5 assessment for years; however, it has obvious data gaps in winter. Here, the Visible Infrared Imaging Radiometer Suite (VIIRS) AOD was applied to supplement MODIS AOD data to obtain a fused AOD dataset. A three-stage model consisting of a corrected AOD model, mixed effects model, and geographically weighted regression model was developed and used with meteorological and vegetation factors to estimate PM2.5. Results showed overall model fitting by cross-validation (CV) with an R2 of 0.92, mean absolute error of 5.72 µg/m3, and root mean square error of 7.15 µg/m3. The combination of MODIS AOD and VIIRS AOD was a suitable method for enhancing AOD coverage. The CV R2 value of the three-stage model (0.92) was higher than that of the two-stage model (0.9). Hence, the three-stage model could achieve a better fit in estimating PM2.5 on a regional scale.

Regional carbon pricing policies focused on specific regions can be used to address local environmental problems. Using China as an example, we extend the spatial coverage of regionally designed policies to cover the entire nation and estimate the costs and benefits of these policies. Based on spatial correlation analysis, a multi-regional dynamic computable general equilibrium, and an extended response surface model with polynomial functions, we explore the co-benefits of regionally-differentiated carbon pricing policies in terms of their impacts on environmental justice and income (in)equality in China by 2060. One national and three regionally-differentiated carbon pricing policies are developed and explored based on 2005–2019 data, including spatial-cluster-level data on PM2.5 concentrations and CO2 emissions and data on average per capita GDP. Results show that the carbon pricing policies achieve China's 2030 carbon mitigation targets and contribute to the 2060 targets of carbon neutrality. However, regionally-differentiated carbon pricing policies result in greater CO2 and air pollutant reductions in their targeted regions compared with the national policy, as CO2 emissions in these regions are priced at higher levels. Regionally-differentiated carbon pricing policy, based on the historical spatial cluster level of PM2.5 concentrations, results in larger reductions in average PM2.5 concentrations in 30 provinces in 2060 compared with other policy scenarios. This policy also promotes improvements in environmental quality and justice across China, which generates additional human health benefits. Furthermore, regional carbon pricing policy has the potential to significantly narrow the income gap between regions. Regionally-differentiated carbon pricing policy designed based on the historical spatial cluster level of PM2.5 concentrations, compared with national policy, generates significant environmental and socio-economic co-benefits in the form of improved environmental justice and reduced income inequality. Regionally-differentiated carbon pricing policy also results in greater reductions in air pollution at a lower cost relative to a national policy and this will generate human health benefits. Results provide insights for other developing countries, especially those that have large disparities in regional economic development, regional income inequality, and air pollution problems. [ABSTRACT FROM AUTHOR]

This study is an attempt to comparatively analyze the impact of renewable energy sources on air quality represented by particulate matter 2.5 concentrations utilizing panel data of 60 countries which are divided into two sub-panels industrialized economies and emerging industrial economies over the period 2010–2019. The study adopts both demand- and supply-side approaches and hence renewable sources are handled in two different structures, i.e., renewable energy consumption and production. Empirical results from both demand- and supply-side regressions strongly confirm the positive impact of renewable sources on air quality in all country groups, meaning that higher renewable energy production and consumption bring about improvement in air quality. In addition, this positive impact of renewables on air quality turned out to be higher in emerging industrial economies than that in industrialized ones. To be more precise, as all control variables are considered, a 10% increase in the production of renewable energy sources brings about a 0.66% improvement in air quality in industrialized economies while its impact is a value of 1.33% in emerging industrial economies. On the other hand, a 10% increase in consumption of renewable energy sources leads to a 0.62% improvement in air quality in industrialized economies and a 1.97% improvement in emerging industrial economies. As for control variables, industrialization gives rise to an increase in air pollution in all country groups, whereas economic growth and trade openness function as favorable factors for air quality. Although population density improves air quality in industrialized economies, it is found as one of the main pollutant factors in emerging industrial economies. Overall results proved that renewable sources improve air quality by reducing particulate matter 2.5 concentrations. Therefore, these countries, especially emerging industrial economies, should replace primitive energy sources like fossil fuels with renewables to bring down environmental degradation up to a reasonable level and increasingly continue to invest in renewable energy domain to reach their environmental sustainability targets. The study also provides some additional policy implications. [ABSTRACT FROM AUTHOR]

Air pollutants, primarily PM2.5, have inflicted significant harm on public health and sustainable urban development in the Yangtze River Economic Belt (YREB). Previous studies often neglected the coordinated measurement of PM2.5 human and natural factors in this area. Therefore, this paper focuses on the YREB. Using a geographic information system (GIS) platform, along with remote sensing and statistical data spanning from 2000 to 2020, this study employs spatial analysis to uncover the spatial-temporal characteristics of PM2.5 and its spatial agglomeration patterns. Furthermore, this study further employs the spatial panel Durbin model to investigate the natural and anthropogenic factors driving PM2.5 concentrations across multiple scales. The analysis of the results reveals an "M"-shaped change trend in PM2.5 concentrations within the YREB. PM2.5 concentrations exhibit significant spatial agglomeration characteristics, whereby most urban agglomerations are high-pollution areas. Moreover, the changes in PM2.5 concentrations are jointly influenced by several factors, including the secondary industry, urban built-up area, population density, annual precipitation, and NDVI. Furthermore, the dominant factors influencing PM2.5 concentrations in the three major urban agglomerations exhibit both similarities and differences. In addition, for effective governance coordination across regions, policymakers should diligently consider both the shared predominant factors and the varying factors specific to each region in the future. This study expands the research content of watershed PM2.5 collaborative governance, and further provides practical support for other watershed environmental governance and urban sustainable management. [ABSTRACT FROM AUTHOR]

The phenomenon of urban shrinkage is currently occurring worldwide; however, the "growth-oriented" planning paradigm is not suitable for these shrinking cities. Reconsidering the relationship between urban form and PM2.5 concentrations from the perspective of urban shrinkage can help provide a research reference for controlling air pollution and optimizing the spatial layout of shrinking cities. This study takes shrinking areas in China as the research subject, which are divided into four research groups according to their shrinkage degree. The empirical results indicate that the average PM2.5 concentrations decrease with the aggravation of urban shrinkage. In terms of the effect of urban form on PM2.5 concentrations, the urban size is always positively related to PM2.5 concentrations, while the impact of urban fragmentation on PM2.5 concentrations is negligible. Further, urban shape positively affects PM2.5 concentrations only in moderately and severely shrinking cities. Cities with sprawling urban forms have higher PM2.5 concentrations, except for those facing severe shrinking trends. This study suggests that governments in shrinking cities should reasonably adjust both the urban form and land use to improve air quality based on the degree of urban shrinkage. [ABSTRACT FROM AUTHOR]

Integrated assessment modelling (IAM) has been successfully used in the development of international agreements to reduce transboundary pollution in Europe, based on the GAINS model of IIASA. At a national level in the UK, a similar approach has been taken with the UK Integrated Assessment Model, UKIAM, superimposing pollution abatement measures and behavioural change on energy projections designed to meet targets set for the reduction of greenhouse gas emissions and allowing for natural and imported contributions from other countries and shipping. This paper describes how the UKIAM was used in the development of proposed targets for the reduction of fine particulate PM2.5 in the UK Environment Act, exploring scenarios encompassing different levels of ambition in reducing the emissions of air pollutants up to 2050, with associated health and other environmental benefits. There are two PM2.5 targets, an annual mean concentration target setting a maximum concentration to be reached by a future year, and a population exposure reduction target with benefits for health across the whole population. The work goes further, also demonstrating links to social deprivation. There is a strong connection between climate measures aimed at reducing net GHG emissions to zero by 2050 and future air quality, which may be positive or negative, as illustrated by sectoral studies for road transport where electrification of the fleet needs to match the evolution of energy production, and for domestic heating, where the use of wood for heating is an air quality issue. The UKIAM has been validated against air pollution measurements and other types of modelling, but there are many uncertainties, including future energy projections. [ABSTRACT FROM AUTHOR]

Climate factors, in addition to human activities, are acknowledged to exert a notable influence on the synoptic PM _2.5 variations over eastern China in extensive case studies. Based on observed daily PM _2.5 concentrations data, this study reveals the enhanced daily variability (DV) of PM _2.5 concentrations over eastern China and identifies its association with mid-latitude sea surface temperature anomalies over the North Atlantic. The dominant daily mode of PM _2.5 concentrations identified through empirical orthogonal function analysis accounts for 43.75% of the total variance, with its DV experienced a significant enhancement from 1979 to 2019. The identified enhancement is attributed to the intensified wave train propagation along the mid-latitudes on a time scale of 10–30 d. The eastward propagation of the identified wave train can expose eastern China to recurrent influences of cyclonic and anticyclonic anomalies, resulting in an initial increase and subsequent decrease in PM _2.5 concentrations. Statistical analysis and dynamic diagnostics show that the warming of the mid-latitude North Atlantic Ocean enhances the wave train at its source through the local energy exchange, and ultimately leads to an increased DV of PM _2.5 concentrations.

In 2017, China introduced the Pilot Zones for Green Finance Reform and Innovations to achieve sustainable and high-quality economic growth. While this policy aims to promote the green transition of businesses and has drawn significant attention, its micro-operational mechanism and firm-level impact remain largely unexplored. This study addresses this research gap by employing a quasi-experimental approach to examine the policy’s effects on companies. The empirical results of our research highlight an unexpected 5.54% surge in the PM _2.5 concentration levels in the vicinity of these firms. Given these findings, we call on policymakers to reflect on the environmental consequences following the roll-out of the Green Finance Reform. Simultaneously, we stress the significance of corporate responsibility in disclosing pertinent indicators and environmental data.

The influence of fresh air unit purification on PM2.5 concentration in indoor environment is a striking problem. In this study, a three-dimensional turbulence model is constructed to analyze the influence of the new air unit purification on the concentration of PM2.5 in the indoor environment of the campus. Firstly, according to the law of dynamics and energy conservation, the motion parameters of PM2.5 particles in campus indoor environment were obtained. Then, the boundary conditions of campus environment are set and the air pressure value of campus space is calculated. Finally, the influence model of fresh air unit purification on PM2.5 concentration in campus indoor environment was constructed. The experimental results show that the accuracy of the model is higher than 87.3%. Moreover, the calculation accuracy of filtration benefit was higher than 87.9%, and the similarity between the calculated results and the measured data is higher than 81%. This study proves that the model can effectively predict the PM2.5 concentration, the filtration benefit of fresh air units and the campus spatial sewage change. Therefore, the model constructed in this study is of high precision, which can provide more accurate results for the indoor air management of campus environment. [ABSTRACT FROM AUTHOR]

Owing to uneven environmental monitoring site distribution, there are significant spatial data gaps for concentrations of ambient fine particles with diameters ≤ 2.5 µm (PM2.5) obtained using traditional monitoring methods. Satellite products are an alternative data source for locations where monitoring sites are unavailable. The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product has been widely used in PM2.5 assessment for years; however, it has obvious data gaps in winter. Here, the Visible Infrared Imaging Radiometer Suite (VIIRS) AOD was applied to supplement MODIS AOD data to obtain a fused AOD dataset. A three-stage model consisting of a corrected AOD model, mixed effects model, and geographically weighted regression model was developed and used with meteorological and vegetation factors to estimate PM2.5. Results showed overall model fitting by cross-validation (CV) with an R2 of 0.92, mean absolute error of 5.72 µg/m3, and root mean square error of 7.15 µg/m3. The combination of MODIS AOD and VIIRS AOD was a suitable method for enhancing AOD coverage. The CV R2 value of the three-stage model (0.92) was higher than that of the two-stage model (0.9). Hence, the three-stage model could achieve a better fit in estimating PM2.5 on a regional scale. [ABSTRACT FROM AUTHOR]

Air pollution is a growing problem and poses a challenge to people's healthy lives. Accurate prediction of air pollutant concentrations is considered the key to air pollution warning and management. In this paper, a novel PM2.5 concentration prediction model, CBAM-CNN-Bi LSTM, is constructed by deep learning techniques based on the principles related to spatial big data. This model consists of the convolutional block attention module (CBAM), the convolutional neural network (CNN), and the bi-directional long short-term memory neural network (Bi LSTM). CBAM is applied to the extraction of feature relationships between pollutant data and meteorological data and assists in deeply obtaining the spatial distribution characteristics of PM2.5 concentrations. As the output layer, Bi LSTM obtains the variation pattern of PM2.5 concentrations from spatial data, overcomes the problem of long-term dependence on PM2.5 concentrations, and achieves the task of accurately forecasting PM2.5 concentrations at multiple sites. Based on real datasets, we perform an experimental evaluation and the results show that, in comparison to other models, CBAM-CNN-Bi LSTM improves the accuracy of PM2.5 concentration prediction. For the prediction tasks from 1 to 12 h, our proposed prediction model performs well. For the 13 to 48 h prediction task, the CBAM-CNN-Bi LSTM also achieves satisfactory results. [ABSTRACT FROM AUTHOR]

Long‐term homogenized haze data are important to understand long‐term changes in air quality. However, there is uncertainty with regard to the trend of haze days due to frequent changes in weather phenomena and visibility observation methods used in China, thereby impairing the understanding of how air quality has varied over time due to global warming. In this study, we analysed historical data on haze days collected by surface weather stations across China and identified several issues in the raw data, including the incorrect identification of floating dust as haze, zero annual records of haze days due to a lack of visibility observations, and incorrect identification of haze as smoke. To correct the affected data, inhomogeneities in the raw annual haze‐day data series since 1980 were resolved by removing floating dust observations from the haze data. Then, automated visibility measurements recorded since 2014 were calibrated according to the percentage of visibility measurements using <10 km as a reference. Finally, based on the corrected and quality‐controlled visibility, relative humidity, and weather data, haze identification was performed to reconstruct daily haze‐day data for China between 1961 and 2020. During this process, automated observation data were homogeneously standardized with former manual observation data. The mean annual haze‐day trends of each region in China showed that the national average number of haze days gradually increased at a rate of 4.27 days per decade between 1961 and 2014, after which it decreased at −1.9 days per year. This trend was verified with the variations in PM2.5 concentrations. Overall, this study presents a novel method for determining the variation and spatial distribution of haze days over the last 60 years in China. The study findings will facilitate future analyses of the spatiotemporal trends of haze days, as well as atmospheric, environmental, and climatic changes over China. [ABSTRACT FROM AUTHOR]

Land use has been demonstrated to have an important influence on PM2.5 concentrations; however, how the scale effects and regional disparities in land use influence PM2.5 concentrations remains unclear. This study investigated the scale differences in spatial variations in PM2.5 concentrations, in spatial associations between PM2.5 concentrations and land use, and explored the effects of the spatial heterogeneity and action scale of land use on PM2.5 concentrations. The main findings indicated greater intra-unit variation at small scales and greater inter-unit variation at large scales. PM2.5 concentrations had a positive association with the surrounding cultivated land and artificial surface, and had a negative association with surrounding forest and grass; the positive spatial association between PM2.5 concentrations and the surrounding artificial surface was stronger at small scales. Cultivated land and forest negatively influenced PM2.5 concentrations generally. Artificial surfaces showed a strong positive influence on PM2.5 concentrations in most urban areas. The action scale of cultivated land in influencing PM2.5 concentrations was the largest (4698.05 m). The findings provide a new interpretation of the relationship between PM2.5 concentrations and land use, and may contribute to effective policy making from the perspective of land use planning to PM2.5 pollution control and abatement. [ABSTRACT FROM AUTHOR]

Concentrations of fine particulate matter smaller than 2.5 μm in diameter (PM2.5) over the Korean Peninsula experience year-to-year variations due to interannual variation in climate conditions. This study develops a multiple linear regression model based on slowly varying boundary conditions to predict winter and spring PM2.5 concentrations at 1–3-month lead times. Nation-wide observations of Korea, which began in 2015, is extended back to 2005 using the local Seoul government's observations, constructing a long-term dataset covering the 2005–2019 period. Using the forward selection stepwise regression approach, we identify sea surface temperature (SST), soil moisture, and 2-m air temperature as predictors for the model, while rejecting sea ice concentration and snow depth due to weak correlations with seasonal PM2.5 concentrations. For the wintertime (December–January–February, DJF), the model based on SSTs over the equatorial Atlantic and soil moisture over the eastern Europe along with the linear PM2.5 concentration trend generates a 3-month forecasts that shows a 0.69 correlation with observations. For the springtime (March–April–May, MAM), the accuracy of the model using SSTs over North Pacific and 2-m air temperature over East Asia increases to 0.75. Additionally, we find a linear relationship between the seasonal mean PM2.5 concentration and an extreme metric, i.e., seasonal number of high PM2.5 concentration days. [ABSTRACT FROM AUTHOR]

Air pollutants, primarily PM2.5, have inflicted significant harm on public health and sustainable urban development in the Yangtze River Economic Belt (YREB). Previous studies often neglected the coordinated measurement of PM2.5 human and natural factors in this area. Therefore, this paper focuses on the YREB. Using a geographic information system (GIS) platform, along with remote sensing and statistical data spanning from 2000 to 2020, this study employs spatial analysis to uncover the spatial-temporal characteristics of PM2.5 and its spatial agglomeration patterns. Furthermore, this study further employs the spatial panel Durbin model to investigate the natural and anthropogenic factors driving PM2.5 concentrations across multiple scales. The analysis of the results reveals an “M”-shaped change trend in PM2.5 concentrations within the YREB. PM2.5 concentrations exhibit significant spatial agglomeration characteristics, whereby most urban agglomerations are high-pollution areas. Moreover, the changes in PM2.5 concentrations are jointly influenced by several factors, including the secondary industry, urban built-up area, population density, annual precipitation, and NDVI. Furthermore, the dominant factors influencing PM2.5 concentrations in the three major urban agglomerations exhibit both similarities and differences. In addition, for effective governance coordination across regions, policymakers should diligently consider both the shared predominant factors and the varying factors specific to each region in the future. This study expands the research content of watershed PM2.5 collaborative governance, and further provides practical support for other watershed environmental governance and urban sustainable management.

The urban form (e.g., city size, shape, scale, density, etc.) can impact the air quality and public health. However, few studies have been conducted to assess the relationship between the urban form and PM2.5 concentrations on a regional scale and long-term basis in China. In this study, we explored the impact of the urban form on the PM2.5 concentrations in four different regions (i.e., northeast, central, east, western) across China for the years 2000, 2005, 2010, and 2015. Five landscape metrics were classified into three characteristics of the urban form (compactness, shape complexity, and urban expansion) using high-resolution remote-sensing data. With considerations given to regional differences, panel-data models and city-level panel data were used to calculate the impact of the urban form on the PM2.5 concentrations. The results of the study indicate that urban expansion is positively correlated with the PM2.5 concentrations across China, with the only exception being the country's western region, which suggests that urban extension is conducive to increasing the PM2.5 levels in relatively developed regions. Meanwhile, the positive relationship between the irregularity of cities and the PM2.5 concentrations indicates that reducing the urban shape complexity will help to mitigate PM2.5 pollution. Moreover, urban compactness, which mainly refers to the landscape-division-index values, proved to have a negative effect on the PM2.5 concentrations, suggesting that the optimization of urban spatial compactness could reduce PM2.5 levels. The findings of this study are beneficial for a better understanding of the intensity and direction of the effect of the urban form on PM2.5 concentrations. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) is a harmful air pollutant that seriously affects public health and sustainable urban development. Previous studies analyzed the spatial pattern and driving factors of PM2.5 concentrations in different regions. However, the spatiotemporal heterogeneity of various influencing factors on PM2.5 was ignored. This study applies the geographically and temporally weighted regression (GTWR) model and geographic information system (GIS) analysis methods to investigate the spatiotemporal heterogeneity of PM2.5 concentrations and the influencing factors in the middle and lower reaches of the Yellow River from 2000 to 2017. The findings indicate that: (1) the annual average of PM2.5 concentrations in the middle and lower reaches of the Yellow River show an overall trend of first rising and then decreasing from 2000 to 2017. In addition, there are significant differences in inter-province PM2.5 pollution in the study area, the PM2.5 concentrations of Tianjin City, Shandong Province, and Henan Province were far higher than the overall mean value of the study area. (2) PM2.5 concentrations in western cities showed a declining trend, while it had a gradually rising trend in the middle and eastern cities of the study area. Meanwhile, the PM2.5 pollution showed the characteristics of path dependence and region locking. (3) the PM2.5 concentrations had significant spatial agglomeration characteristics from 2000 to 2017. The "High-High (H-H)" clusters were mainly concentrated in the southern Hebei Province and the northern Henan Province, and the "Low-Low (L-L)" clusters were concentrated in northwest marginal cities in the study area. (4) The influencing factors of PM2.5 have significant spatiotemporal non-stationary characteristics, and there are obvious differences in the direction and intensity of socio-economic and natural factors. Overall, the variable of temperature is one of the most important natural conditions to play a positive impact on PM2.5, while elevation makes a strong negative impact on PM2.5. Car ownership and population density are the main socio-economic influencing factors which make a positive effect on PM2.5, while the variable of foreign direct investment (FDI) plays a strong negative effect on PM2.5. The results of this study are useful for understanding the spatiotemporal distribution characteristics of PM2.5 concentrations and formulating policies to alleviate haze pollution by policymakers in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

This paper analyzes the dynamic impact of economic, social, and governance factors on PM2.5 concentrations in 89 countries from 2006 to 2019. Using the GMM-PVAR approach and Impulse-Response Functions, we examine how shocks to specific variables affect PM2.5 concentrations over a 10-year period. Our findings reveal that the influence of these factors on PM2.5 levels varies over time. For example, a shock in urbanization has no effect on PM2.5 concentrations in the first year, but in the second year, pollution increases significantly. In the third period, PM2.5 levels decrease, but they rise again in the fourth period, albeit not significantly. By the fifth period, pollution decreases until a new equilibrium is reached in the sixth period. Additionally, a shock in financial development, government effectiveness, industrialization, trade openness, or GDP has no effect on PM2.5 concentrations in the initial period. However, during the second period, air pollution decreases, followed by an increase in the third period and a decrease again in the fourth period. These dynamic patterns highlight the need for environmental policies that consider the evaluation time horizon. Our analysis is supplemented by the Granger causality test, guiding specific policy recommendations based on our findings. [Display omitted] • The highest concentrations of PM2.5 are observed in Tajikistan, Cameroon, Niger, and Egypt. • The lowest PM2.5 concentrations are found in Norway, Sweden, and Finland. • Reducing air pollution has a significant impact on urbanization levels in countries. • Changes in government effectiveness significantly affect PM2.5 concentrations. • GDP influences PM2.5 concentrations with feedback effects. [ABSTRACT FROM AUTHOR]

The central region of Inner Mongolia is the northern ecological safety barrier of Beijing and even the whole country. It is one of the main sources of dust in North China, and air pollution control is the top priority in this region. In this study, the central region of Inner Mongolia was selected as the study area, multiple auxiliary variables were used to estimate the spatial distribution of PM2.5 concentration from 2016 to 2018 by geographically weighted regression, and the socioeconomic determinants of PM2.5 concentration were analyzed by geographic detectors. The results show that: 1) the established model can better estimate the spatial distribution of PM2.5 concentration in the study area, and the monthly mean correlation coefficient R of the verification parameters is stable, ranging from 0.58 to 0.66. 2) PM2.5 concentration in central Inner Mongolia showed significant temporal and spatial variation. The mean annual PM2.5 concentration along the Yellow River basin is the highest in the study area. PM2.5 concentration first increased and then decreased from 2016 to 2018. 3) Urban built-up area, permanent population and per capita GDP are the key factors affecting the spatial and temporal distribution of PM2.5 concentration in the study area. The results of this study provide theoretical basis and technical support for air pollution monitoring, management and prevention in central Inner Mongolia.

Industrial land is a prerequisite for industrial development and a crucial factor in promoting local economic growth. However, while promoting industrial development, industrial land expansion has produced much environmental pollution, but its impact has not been effectively evaluated. Based on the balanced panel data of 324 cities in China from 2007 to 2016, the effect of industrial land expansion on PM2.5 concentrations is studied using a spatial econometric model. The results show the evident regional heterogeneity of industrial land expansion. The eastern region has the largest expansion scale. Additionally, since 2013, the scale of industrial land expansion has decreased. The vast cities in the central and eastern regions have relatively high PM2.5 concentrations, especially in the North China Plain. Furthermore, the overall PM2.5 concentrations show a downward trend; however, a noticeable interannual fluctuation has been observed. Besides, the industrial land expansion has significantly promoted increased PM2.5 concentrations, both nationally and regionally. The most significant impact is in the eastern region, and the smallest impact is in the central region. Population density (POPD), road density (RORD), and temperature (TEM) positively correlate with PM2.5 concentrations, whereas economic development levels (EDL), industrial structure upgrading (ISU), vegetation coverage (NDVI), and precipitation (PRE) have varying effects on PM2.5 concentrations in each region.

Despite concerted efforts in emission control, air pollution control remains challenging. Urban planning has emerged as a crucial strategy for mitigating PM 2.5 pollution. What remains unclear is the impact of urban form and their interactions with seasonal changes. In this study, base on the air quality monitoring stations in the Yangtze River Delta urban agglomeration, the relationship between urban spatial indicators (building morphology and land use) and PM 2.5 concentrations was investigated using full subset regression and variance partitioning analysis, and seasonal differences were further analysed. Our findings reveal that PM 2.5 pollution exhibits different sensitivities to spatial scales, with higher sensitivity to the local microclimate formed by the three-dimensional structure of buildings at the local scale, while land use exerts greater influence at larger scales. Specifically, land use indicators contributed sustantially more to the PM 2.5 prediction model as buffer zone expand (from an average of 2.41% at 100 m range to 47.30% at 5000 m range), whereas building morphology indicators display an inverse trend (from an average of 13.84% at 100 m range to 1.88% at 5000 m range). These results enderscore the importance of considering building morphology in local-scale urban planning, where the increasing building height can significantly enhance the disperion of PM 2.5 pollution. Conversely, large-scale urban planning should prioritize the mixed use of green spaces and construction lands to mitigate PM 2.5 pollution. Moreover, the significant seasonal differences in the ralationship between urban spatical indicatiors and PM 2.5 pollution were observed. Particularly moteworthy is the heightened association between forest, water indicators and PM 2.5 concentrations in summer, indicating the urban forests may facilitate the formation of volatile compunds, exacerbating the PM 2.5 pollution. Our study provides a theoretical basis for addressing scale-related challenges in urban spatial planning, thereby forstering the sustainable development of cities. • Building structures significantly influence PM 2.5 locally, emphasizing the role of microclimates in pollution dynamics. • Land use's role in PM 2.5 pollution grows with buffer zone expansion, while building morphology's impact lessens over distance. • Urban forests, water indicators have a stronger link to PM 2.5 in summer, urging tailored planning for different seasons. [ABSTRACT FROM AUTHOR]

Millions of Ethiopian people cook with biomass fuels using traditional stoves, releasing harmful pollutants and contributing to a significant public health crisis. Improved stoves offer a potential escape route, but their effectiveness needs close scrutiny. This study delves into the impact of chimney-fitted stoves on kitchen PM 2.5 concentrations in rural Ethiopian households. We conducted a randomized controlled trial with 86 households equally divided (1:1 ratio) between intervention and control groups. The 24-h average kitchen PM 2.5 concentrations was measured using Particle and Temperature Sensor (PATS+) at baseline and after intervention. All relevant sociodemographic and cooking related characteristics were collected at baseline and dynamic characteristics were updated during air monitoring visits. Three distinct statistical models, including independent sample t-tests, paired sample t-tests and one-way analysis of variance were used to analyze the data using Statistical Package for the Social Sciences (SPSS) software for Windows (v 24.0). At baseline, the average 24-h kitchen PM 2.5 concentrations were 482 μg/m3 (95% CI: 408, 557) for the control and 405 μg/m3 (95% CI: 318, 492) for the intervention groups. Despite remaining elevated at 449 μg/m3 (95% CI: 401, 496) in the control group, PM 2.5 concentrations reduced to 104 μg/m3 (95% CI: 90,118) in the intervention group, indicating a statistically significant difference (t = 6.97, p < 0.001). All three statistical analyses delivered remarkably consistent results, estimating a PM 2.5 reductions of 74% with the before-and-after approach, 76% when comparing groups, and 74% for difference in difference analysis. Beyond the overall reduction, homes with primary school completed women, larger kitchens, smaller family size, and those specifically baking Injera (the traditional energy-intensive staple food), witnessed even greater drops in PM 2.5 levels. Pregnant women in our study encountered dangerously high PM 2.5 exposures in their kitchens. While the intervention achieved a significant PM 2.5 reductions, unfortunately remained above the WHO's safe limit, highlighting the need for further interventions. [Display omitted] • Pregnant women in rural Ethiopia face substantial exposure to harmful kitchen smoke from burning biomass fuels. • Chimneys installed on Mirt stoves significantly reduce the concentration of fine particulate matter (PM 2.5) in rural Ethiopian kitchens. • Although chimney-equipped Mirt stoves significantly decrease PM 2.5 levels, post-intervention concentrations in kitchens still exceed the WHO's recommended levels. • Though it's important to address the underlying issue of harmful cooking practices and ventilation deficiencies, replacing thatched roofs with corrugated iron roofs in rural kitchens can be considered as a strategy to reduce smoke exposure. [ABSTRACT FROM AUTHOR]

Integrated assessment modelling (IAM) has been successfully used in the development of international agreements to reduce transboundary pollution in Europe, based on the GAINS model of IIASA. At a national level in the UK, a similar approach has been taken with the UK Integrated Assessment Model, UKIAM, superimposing pollution abatement measures and behavioural change on energy projections designed to meet targets set for the reduction of greenhouse gas emissions and allowing for natural and imported contributions from other countries and shipping. This paper describes how the UKIAM was used in the development of proposed targets for the reduction of fine particulate PM2.5 in the UK Environment Act, exploring scenarios encompassing different levels of ambition in reducing the emissions of air pollutants up to 2050, with associated health and other environmental benefits. There are two PM2.5 targets, an annual mean concentration target setting a maximum concentration to be reached by a future year, and a population exposure reduction target with benefits for health across the whole population. The work goes further, also demonstrating links to social deprivation. There is a strong connection between climate measures aimed at reducing net GHG emissions to zero by 2050 and future air quality, which may be positive or negative, as illustrated by sectoral studies for road transport where electrification of the fleet needs to match the evolution of energy production, and for domestic heating, where the use of wood for heating is an air quality issue. The UKIAM has been validated against air pollution measurements and other types of modelling, but there are many uncertainties, including future energy projections.

Environmental issues, particularly air pollution, are a matter of concern for people all around the world. PM2.5 levels that are too high harm people's physical and mental health. For government air pollution control, more accurate PM2.5 concentration predictions are critical. In this paper, we explored the relationship between pollutants (PM10, SO2, NO2, O3, CO) and meteorological factors (atmospheric pressure, relative humidity, air temperature, wind speed, wind direction, cumulative precipitation) that affect the generation and transmission of PM2.5. To better predict the concentration of PM2.5, we innovatively combined principal component analysis (PCA) and clustering methods to extract pollutant variables and patterns as important PM2.5 concentration predictors of different models such as support vector regression (SVR), multivariate nonlinear regression (MNR), and artificial neural network (ANN). Compared to MNR and ANN models, SVR presented better prediction accuracy. Moreover, cuckoo search (CS), cross-validation (CV), and particle swarm optimization (PSO) algorithms were used to further optimize the parameters in the process of SVR. And to evaluate the above PM2.5 concentration prediction results, we introduced several evaluating indicators including root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and person correlation coefficient (R) between predicted and measured values. The obtained results confirmed that when the pollutant data was divided into three patterns, the best prediction accuracy was achieved by the CSSVR model. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) seriously affects the environment, climate, and human health. Over the past decades, the Beijing–Tianjin–Hebei region (BTH) has been severely affected by pollutant gas and PM2.5 emissions caused by heavy industrial production, topography, and other factors and has been one of the most polluted areas in China. Currently, the long-term, large-scale, and high spatial resolution monitoring PM2.5 concentrations ([PM2.5]) using satellite remote sensing technology is an important task for the prevention and control of air pollution. The aerosol optical depth (AOD) retrieved by satellites combined with a variety of auxiliary information was widely used to estimate [PM2.5]. In this study, a two-stage statistical regression [linear mixed effects (LME) + geographically weighted regression (GWR)] model, combined with the latest high spatial resolution (1 km) AOD product and meteorological and land use parameters, was constructed to estimate [PM2.5] in BTH from 2013 to 2020. The model was fitted annually, and the ranges of coefficient of determination (R2), root mean square prediction errors (RMSPE), and relative prediction error (RPE) for the model cross-validation were 0.85–0.95, 7.87–29.90 μg/m3, and 19.19%–32.71%, respectively. Overall, the model obtained relatively good performance and could effectively estimate [PM2.5] in BTH. The [PM2.5] showed obvious temporal characteristic within a year (high in winter and low in summer) and spatial characteristic (high in the southern plain and low in the northern mountain). During the investigated period of 2013–2020, the high pollutant areas ([PM2.5] > 75 μg/m3) in 2020 significantly narrowed compared to 2013, and the annual average [PM2.5] in BTH fell below 55 μg/m3, with a drop of 54.04%. In particular, the [PM2.5] in winter season dropped sharply from 2015 to 2017 and declined steadily after 2017. Our results suggested that significant achievements have been made in air pollution control over the past 8 years, and they still need to be maintained. The research can provide scientific basis and support for the prevention and control of air pollution in BTH and beyond.

Based on panel data on 248 prefecture-level cities in China from 2003 to 2018, this study first estimates the treatment effect of the Shanxi Comprehensive Reform Area policy on PM2.5 concentrations using a PSM-DID method within the counterfactual framework. The average treatment effect shows that contrary to the naive before-after analysis, on average, the Shanxi Comprehensive Reform Area policy significantly increased the PM2.5 concentrations of prefecture-level cities in Shanxi Province by 0.211% annually, and the place-based placebo test shows that the treatment effect obtained above is robust. Second, the dynamic treatment effects show a continuous decrease in incremental effects during 2011–2018, gradually decreasing from a significant positive increment during 2011–2015 to a zero or even a negative increment during 2016–2018, indicating that the Shanxi Comprehensive Reform Area policy gradually increased in environmental friendliness. Third, the mediating effects estimated by the causal steps procedure show that the Shanxi Comprehensive Reform Area policy influenced PM2.5 concentrations by increasing the intensity of resource exploitation and decreasing the intensity of environmental regulations, but the capacity of scientific and technological innovations had no mediating effect on the relationship between the policy and PM2.5 concentrations. Therefore, the government should further reduce the intensity of resource exploitation, strengthen the intensity of environmental regulations, and promote environmentally focused scientific and technological innovations to reduce PM2.5 concentrations in Shanxi Province. [ABSTRACT FROM AUTHOR]

Rapid urbanization has caused serious PM2.5 pollution. The existing researches mainly focus on the contribution of one aspect of urbanization to PM2.5 concentrations. However, few studies have comprehensively considered the direct and indirect effects of multi-dimensional urbanization on PM2.5 concentrations. In order to identify the impact of multi-dimensional urbanization on PM2.5 concentrations, the entropy-right method, the spatial lag model, spatial error model and spatial Durbin model were used to investigate the relationships between multi-dimensional urbanization and PM2.5 concentrations in 261 cities of China in 2016. Results demonstrated that the spillover effect of PM2.5 concentrations between different cities was significant. Economic urbanization had the strongest direct effect on PM2.5 concentrations. Social urbanization and ecological urbanization had a significantly negative impact on PM2.5 concentrations in local cities. However, the biggest indirect effect on PM2.5 concentrations in adjacent cities was social urbanization, followed by land urbanization. The direct and indirect effects of population urbanization on PM2.5 concentrations were non-significant and the least. The findings from this result analysis and discussion can be used as a theoretical basis to provide some policy suggestions for China's air governance and sustainable urban development.

Given the serious threat of PM2.5 pollution to the environment and public health in China, reducing PM2.5 concentrations through urban planning practices has become a common interest of urban planners and policymakers. In this regard, identifying the relationship between urban form and PM2.5 concentrations has been a matter of much concern. This paper seeks to investigate how urban form influences PM2.5 concentrations by taking the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as a case study. First, the urban form was assessed by two indicators, i.e., the patch complexity index (PCI) and urban compactness index (UCI), which were developed based on multiple landscape metrics and the principal component analysis. Then, the relationship between PM2.5 concentrations and the two urban form indicators was revealed by spatial panel models. Our results mainly showed that PCI and UCI were negatively and positively associated with PM2.5 concentrations, respectively, indicating that less compact urban forms with high patch complexity had advantages in reducing the PM2.5 concentrations. According to these empirical results, the mixed-use urban development with high green coverage, which is conducive to constructing a more complex and less compact urban form, would be adoptable in terms of mitigating PM2.5 concentrations in the GBA urban agglomeration.

We examined PM 2.5 and Hazard Mapping System smoke plume satellite data at ∼600 United States (US) air monitoring stations to identify surface smoke on 14.0% of all May-September days for 2018-2023, with large influences in 2020 and 2021, due to California fires, and 2023, due to Canadian fires. Days with smoke have an average of 11 μg m -3 more PM 2.5 and 8 ppb higher maximum daily 8 h average (MDA8) O 3 concentrations than nonsmoke days, and they also account for 94% of all days that exceed the daily PM 2.5 health standard (35 μg m -3 ) and 36% of all days that exceed the O 3 health standard (70 ppb). To estimate the smoke contributions to the O 3 MDA8, Generalized Additive Models (GAMs) were built for each site using the nonsmoke day data and up to 8 predictors. The mean and standard deviation of the residuals from the GAMs were 0 ± 6.1 ppb for the nonsmoke day data and 4.3 ± 7.9 ppb for the smoke day data, indicating a significant enhancement in the MDA8 O 3 on smoke days. We found positive residuals on 72% of the smoke days and for these days, we calculate an average smoke contribution to the O 3 MDA8 of 7.8 ± 6.0 ppb. Over the 6 year period, the percentage of exceedance days due to smoke in the continental US was 25% of all exceedance days, and the highest was in 2023 (38%). In 2023, the Central US experienced an unusually high number of exceedance days, 1522, with 52% of these impacted by smoke, while the Eastern US had fewer exceedance days, 288, with 78% of these impacted by smoke. Our results demonstrate the importance of wildland fires as contributors to exceedances of the health-based national air quality standards for PM 2.5 and O 3 .

Clean energy transition has been considered as an indispensable way to attain sustainable development for China, where the coal-to-gas initiative plays a vital role towards the goal. This paper takes Beijing, China's political and economic center as well as a national pioneer in the energy transition, as a case to systematically analyze the co-mitigation of air pollution (PM2.5) and carbon emissions (CO2) achieved by the policy-driven natural gas-coal consumption substitution. Firstly, a qualitative analysis of the relationship of Beijing's coal-to-gas policies and its air quality has been conducted. Then, VAR and ARDL models are employed to quantitatively analyze the impacts of coal-to-gas policies on PM2.5 and CO2, respectively. Results show that (i) an innovation of natural gas/coal consumption ratio will reduce PM2.5 concentrations, and the effect decreases over time; and (ii) an increase of 1% in natural gas/coal consumption ratio in Beijing will cause a decrease of 0.0784% in CO2 emissions in the long run. Therefore, the coal-to-gas policies do increase the usage of natural gas and improve Beijing's air quality. The assessment methods and conclusions can be regarded as a reference for not only China's policymakers, but also other countries, especially nowadays when air quality is becoming more valued and GHGs are being tightly controlled. [ABSTRACT FROM AUTHOR]

Air pollution is a growing problem and poses a challenge to people’s healthy lives. Accurate prediction of air pollutant concentrations is considered the key to air pollution warning and management. In this paper, a novel PM2.5 concentration prediction model, CBAM-CNN-Bi LSTM, is constructed by deep learning techniques based on the principles related to spatial big data. This model consists of the convolutional block attention module (CBAM), the convolutional neural network (CNN), and the bi-directional long short-term memory neural network (Bi LSTM). CBAM is applied to the extraction of feature relationships between pollutant data and meteorological data and assists in deeply obtaining the spatial distribution characteristics of PM2.5 concentrations. As the output layer, Bi LSTM obtains the variation pattern of PM2.5 concentrations from spatial data, overcomes the problem of long-term dependence on PM2.5 concentrations, and achieves the task of accurately forecasting PM2.5 concentrations at multiple sites. Based on real datasets, we perform an experimental evaluation and the results show that, in comparison to other models, CBAM-CNN-Bi LSTM improves the accuracy of PM2.5 concentration prediction. For the prediction tasks from 1 to 12 h, our proposed prediction model performs well. For the 13 to 48 h prediction task, the CBAM-CNN-Bi LSTM also achieves satisfactory results.

Land use has been demonstrated to have an important influence on PM2.5 concentrations; however, how the scale effects and regional disparities in land use influence PM2.5 concentrations remains unclear. This study investigated the scale differences in spatial variations in PM2.5 concentrations, in spatial associations between PM2.5 concentrations and land use, and explored the effects of the spatial heterogeneity and action scale of land use on PM2.5 concentrations. The main findings indicated greater intra-unit variation at small scales and greater inter-unit variation at large scales. PM2.5 concentrations had a positive association with the surrounding cultivated land and artificial surface, and had a negative association with surrounding forest and grass; the positive spatial association between PM2.5 concentrations and the surrounding artificial surface was stronger at small scales. Cultivated land and forest negatively influenced PM2.5 concentrations generally. Artificial surfaces showed a strong positive influence on PM2.5 concentrations in most urban areas. The action scale of cultivated land in influencing PM2.5 concentrations was the largest (4698.05 m). The findings provide a new interpretation of the relationship between PM2.5 concentrations and land use, and may contribute to effective policy making from the perspective of land use planning to PM2.5 pollution control and abatement.

Source apportionment and the effect of reducing individual sources is important input for the development of strategies to address air pollution. The UK Integrated Assessment Model, UKIAM, has been developed for this purpose as a flexible framework, combining information from different atmospheric dispersion models to cover different pollutant contributions, and span the range from European to local scale. In this paper we describe the UKIAM as developed for SO2, NOx, NH3, PM2.5 and VOCs. We illustrate its versatility and application with assessment of current PM2.5 concentrations and exposure of the UK population, as a case-study that has been used as the starting point to investigate potential improvement towards attainment of the WHO guideline of 10 µg/m3.

The main objective of this study is to simulate the hourly concentrations of the PM2.5 concentrations using the Multiple Linear Regression (MLR) model for the selected sea breeze days in Split, Croatia. Stepwise adjustment is used for the selection of predictors. A predictor characteristic to the daily and nightly part of the coastal circulation, calculated as hourly temperature change to the temperature at the time of the sea breeze lulls, was found to be significant for PM2.5 particles during sea breeze. The mean monthly values of the MLR model simulated and measured PM2.5 hourly concentrations for the selected sea breeze cases were simulated relatively well. The hourly simulations also show a very good fit with the hourly measurements, and the index of agreement (IA) is 0.9 for the daily and 0.8 for the nightly part of the coastal circulation. [ABSTRACT FROM AUTHOR]

The urban form (e.g., city size, shape, scale, density, etc.) can impact the air quality and public health. However, few studies have been conducted to assess the relationship between the urban form and PM2.5 concentrations on a regional scale and long-term basis in China. In this study, we explored the impact of the urban form on the PM2.5 concentrations in four different regions (i.e., northeast, central, east, western) across China for the years 2000, 2005, 2010, and 2015. Five landscape metrics were classified into three characteristics of the urban form (compactness, shape complexity, and urban expansion) using high-resolution remote-sensing data. With considerations given to regional differences, panel-data models and city-level panel data were used to calculate the impact of the urban form on the PM2.5 concentrations. The results of the study indicate that urban expansion is positively correlated with the PM2.5 concentrations across China, with the only exception being the country’s western region, which suggests that urban extension is conducive to increasing the PM2.5 levels in relatively developed regions. Meanwhile, the positive relationship between the irregularity of cities and the PM2.5 concentrations indicates that reducing the urban shape complexity will help to mitigate PM2.5 pollution. Moreover, urban compactness, which mainly refers to the landscape-division-index values, proved to have a negative effect on the PM2.5 concentrations, suggesting that the optimization of urban spatial compactness could reduce PM2.5 levels. The findings of this study are beneficial for a better understanding of the intensity and direction of the effect of the urban form on PM2.5 concentrations.

Background: Particulate pollution is currently regarded as a severe environmental problem, which is intimately linked to reductions in air quality and human health, as well as global climate change. Objective: Accurately identifying the key factors that drive air pollution is of great significance. The temporal and spatial heterogeneity of such factors is seldom taken into account in the existing literature. Method: In this study, we adopted a geographically and temporally weighted regression model (GTWR) to explore the direction and strength of the influences of natural conditions and socioeconomic issues on the occurrence of PM2.5 pollutions in 287 Chinese cities covering the period 1998 to 2015. Result: Cities with serious PM2.5 pollution were discovered to mainly be situated in northern China, whilst cities with less pollution were shown to be located in southern China. Higher temperature and wind speed were found to be able to alleviate air pollution in the country’s southeast, where enhanced precipitation was also shown to reduce PM2.5 concentrations; whilst in southern and central and western regions, precipitation and PM2.5 concentrations were positively correlated. Increased relative humidity was found to reinforce PM2.5 concentration in southwest and northeast China. Furthermore, per capita GDP and population density were shown to intensify PM2.5 concentrations in northwest China, inversely, they imposed a substantial adverse effect on PM2.5 concentration levels in other areas. The amount of urban built-up area was more positively associated with PM2.5 concentration levels in southeastern cities than in other cities in China. Conclusion: PM2.5 concentrations conformed to a series of stages and demonstrated distinct spatial differences in China. The associations between PM2.5 concentration levels and their determinants exhibit obvious spatial heterogeneity. The findings of this paper provide detailed support for regions to formulate targeted emission mitigation policies.