Spatial and temporal analysis of Air Pollution Index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001-2011

SDIPPWV: A novel hybrid prediction model based on stepwise decomposition-integration-prediction avoids future information leakage to predict precipitable water vapor from GNSS observations

Water vapor is an important meteorological parameter. Accurate prediction of water vapor content can be used to provide important reference information for heavy rainfall forecast and artificial precipitation operation. The current water vapor hybrid prediction model has the problem of future data leakage, and the error is accumulated by reconstructing the subsequence after prediction. Therefore, this paper proposes a stepwise decomposition-integration-prediction precipitable water vapor mechanism, named SDIPPWV, which can effectively solve the above problems. Firstly, High-precision precipitable water vapor (PWV) sequence is retrieved from Global Navigation Satellite System (GNSS) observation files. Then stepwise decomposition process uses a fixed-size window to segment the PWV sequence and Seasonal-Trend decomposition based on Loess (STL) to decompose the sequences within the window. Next, the features of the three sub-sequences are integrated to construct the feature space. Finally the prediction of PWV is obtained using 1D Convolutional Neural Network-Bidirectional Long Short Term Memory (1D CNN-BiLSTM). The model performance is verified using observation data from eight GNSS stations. The performance of the PWV prediction model proposed in this paper is effectively improved compared with the single prediction models and other hybrid models. The mean root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2) of the eight stations are 0.2146 mm, 0.1132 mm, 1.29 %, and 0.9998, respectively. The results show that the model proposed in this paper improves the prediction accuracy of water vapor content while solving the data leakage problem.

Measurement of the vertical distributions of atmospheric pollutants using an uncrewed aerial vehicle platform in Xi'an, China

Vertical observations of atmospheric pollutants play crucial roles in a comprehensive understanding of the distribution characteristics and transport of atmospheric pollutants. A hexacopter uncrewed aerial vehicle equipped with miniature monitors was employed to measure the vertical distribution of atmospheric pollutants within a height of 1000 m at a rural site in Xi'an, China, in 2021. The concentrations of carbon monoxide (CO) and particulate matter (PM) showed generally decreasing trends with increasing height. The ozone (O3) concentration showed a general increasing trend with height followed by a gradual decreasing trend. Vertical decrements of PM2.5 and CO from 0 to 1000 m were significantly (p < 0.05) lower on observation days during summer (14.0 ± 8.1 μg m-3 and 8.7 ± 6.6 ppb, respectively), compared with those in winter (78.3 ± 14.1 μg m-3 and 34.8 ± 17.3 ppb, respectively). The horizontal transport of PM and CO mostly occurred in the morning and at night during winter observations at an altitude of 400-500 m. During the winter haze, the PM and CO profile concentrations below 500 m increased substantially with the decrease in the height of the thermal inversion layer. Vertical O3 transportation was observed in the afternoon and evening during summer, and a ∼37.7% (11.6 ppb) increase in ground-level O3 was observed in relation to vertical transport from the upper atmosphere. The results provide insights into the vertical distribution and transport of atmospheric pollutants in rural areas near cities.

Phosphocholine-induced energy source shift alleviates mitochondrial dysfunction in lung cells caused by geospecific PM2.5 components

Fine particulate matter (PM2.5) is globally recognized for its adverse implications on human health. Yet, remain limited the individual contribution of particular PM2.5 components to its toxicity, especially considering regional disparities. Moreover, prevention solutions for PM2.5-associated health effects are scarce. In the present study, we comprehensively characterized and compared the primary PM2.5 constituents and their altered metabolites from two locations: Taiyuan and Guangzhou. Analysis of year-long PM2.5 samples revealed 84 major components, encompassing organic carbon, elemental carbon, ions, metals, and organic chemicals. PM2.5 from Taiyuan exhibited higher contamination, associated health risks, dithiothreitol activity, and cytotoxicities than Guangzhou's counterpart. Applying metabolomics, BEAS-2B lung cells exposed to PM2.5 from both cities were screened for significant alterations. A correlation analysis revealed the metabolites altered by PM2.5 and the critical toxic PM2.5 components in both regions. Among the PM2.5-down-regulated metabolites, phosphocholine emerged as a promising intervention for PM2.5 cytotoxicities. Its supplementation effectively attenuated PM2.5-induced energy metabolism disorder and cell death via activating fatty acid oxidation and inhibiting Phospho1 expression. The highlighted toxic chemicals displayed combined toxicities, potentially counteracted by phosphocholine. Our study offered a promising functional metabolite to alleviate PM2.5-induced cellular disorder and provided insights into the geo-based variability in toxic PM2.5 components.

Spatiotemporal empirical analysis of particulate matter PM2.5 pollution and air quality index (AQI) trends in Africa using MERRA-2 reanalysis datasets (1980-2021)

In this study, the spatial-temporal trends of PM2.5 pollution were analyzed for subregions in Africa and the entire continent from 1980 to 2021. The distributions and trends of PM2.5 were derived from the monthly concentrations of the aerosol species from MERRA-2 reanalysis datasets comprising of sulphates (SO4), organic carbon (OC), black carbon (BC), Dust2.5 and Sea Salt (SS2.5). The resulting PM2.5 trends were compared with the climate factors, socio-economic indicators, and terrain characteristics. Using the Mann-Kendall (M-K) test, the continent and its subregions showed positive trends in PM2.5 concentrations, except for western and central Africa which exhibited marginal negative trends. The M-K trends also determined Dust2.5 as the dominant contributing aerosol factor responsible for the high PM2.5 concentrations in the northern, western and central regions of Africa, while SO4 and OC were respectively the most significant contributors to PM2.5 in the eastern and southern Africa regions. For the climate factors, the PM2.5 trends were determined to be positively correlated with the wind speed trends, while precipitation and temperature trends exhibited low and sometimes negative correlations with PM2.5. Socio-economically, highly populated, and bare/sparse vegetated areas showed higher PM2.5 concentrations, while vegetated areas tended to have lower PM2.5 concentrations. Topographically, low laying regions were observed to retain the deposited PM2.5 especially in the northern and western regions of Africa. The Air Quality Index (AQI) results showed that 94 % of the continent had an average PM2.5 of 12-35 μg/m3 hence classified as "Moderate" AQI, and the rest of the continent's PM2.5 levels was between 35 and 55 μg/m3 implying AQI classification of "Unhealthy for Sensitive People". Northern and western Africa regions had the highest AQI, while southern Africa had the lowest AQI. The approach and findings in this study can be used to complement the evaluation and management of air quality in Africa.

The Air Quality Index (AQI) in historical and analytical perspective a tutorial review

The Air Quality Index (AQI), developed by the United States Environmental Protection Agency (USEPA), has been providing the public with crucial information regarding the status of contamination of the atmosphere for the past 45 years. Prior to introduction of the AQI, only a handful of metropolitan areas reported on air quality, and each region decided on its own metric. The inception of a single AQI helped homogenize the air quality metrics across the nation and indeed served as an important future template for other governmental and regulatory agencies across the world. The formulators had the foresight to recognize that our understanding of air pollution and its effects may change over time, which are likely to change regulatory limits. They used a dynamic framework to define the AQI, such that the broad definition or principle does not need to change with every change in regulatory limits or policy, and the fundamental goal of alerting the public to deleterious air quality is not affected. The establishment of the AQI increased public awareness of the importance of clean air and has helped muster support for air quality and emission regulations. The National Ambient Air Quality Standards (NAAQS) set forth by the USEPA provides acceptable levels of criteria pollutants - namely carbon monoxide, lead, nitrogen dioxide, ozone, particulate matter, and sulfur dioxide. A comparison of the actual levels, as compared to the regulatory limits (since the cessation of leaded gasoline, lead is no longer included in the index), are used as the basis for the AQI. As the regulatory limits change, so does the exact evaluation of the AQI, making it a living index. In this paper, we provide a historical overview of the Air Quality Index, the Federal Reference Methods (FRMs) vs. Federal Equivalent Methods (FEMs) for measuring them, and as an illustrative example, we discuss the air quality for Dallas-Ft. Worth, currently the fourth most populous metropolitan region in the United States, vis-a-vis the reported AQI.

Synergistic relationship and interact driving factors of pollution and carbon reduction in the Yangtze River Delta urban agglomeration, China

The urban agglomeration is the most concentrated region of economy, population, and industry. It is also the key area of carbon emissions (CE) and air pollution management. CE and air pollution have the possibility of collaborative governance due to the same root and the same source of them. To achieve the goal of sustainable development, it is important to study the coordinated relationship of CE and atmosphere pollutants in urban agglomerations. However, most researches have ignored the synergistic relationship between CE and air pollutants. Furthermore, there is limited current study on the driving factors of the synergistic relationship between air pollutants and CE. To fill these research gaps, we first explore the spatial-temporal evolvement law of CE and PM2.5 utilizing satellite remote sensing data sets. Secondly, we analyze the synergistic relationship of CE and PM2.5 in the Yangtze River Delta (YRD) urban agglomeration using the coupling coordination degree (CCD) model from 2000 to 2020. At last, we further study the influencing factors of the synergistic relationship of CE and PM2.5 based on the geo-detector model. The findings display that (1) in 2020, the total CE in the YRD urban agglomeration is 2.24 billion tons, accounting for 22.5% of China, but its growth rate has gradually dropped to 7.25%. Besides, the PM2.5 concentration shows a waving upward-downward tendency. In 2020, the range of higher PM2.5 regions significantly decreased, and air quality gradually improved. (2) The CCD of PM2.5 and CE is at the coordination level in general (CCD > 0.6) between 2000 and 2020, which can realize the coordinated governance of pollution and carbon reduction. (3) Digital elevation model (DEM), topographic relief (RDLS), and population density have a higher degree of influence on the synergistic relationship between CE and PM2.5. Besides, the interaction of topographic and socio-economic factors is the main driving factor between the two. This paper can provide a referral for decision-makers to synergistically make plans for pollution and carbon reduction and facilitate the sustainable development of urban agglomerations.

Quantifying the scale-dependent relationships of PM2.5 and O3 on meteorological factors and their influencing factors in the Beijing-Tianjin-Hebei region and surrounding areas

To investigate the variations of PM2.5 and O3 and their synergistic effects with influencing factors at different time scales, we employed Bayesian estimator of abrupt seasonal and trend change to analyze the nonlinear variation process of PM2.5 and O3. Wavelet coherence and multiple wavelet coherence were utilized to quantify the coupling oscillation relationships of PM2.5 and O3 on single/multiple meteorological factors in the time-frequency domain. Furthermore, we combined this analysis with the partial wavelet coherence to quantitatively evaluate the influence of atmospheric teleconnection factors on the response relationships. The results obtained from this comprehensive analysis are as follows: (1) The seasonal component of PM2.5 exhibited a change point, which was most likely to occur in January 2017. The trend component showed a discontinuous decline and had a change point, which was most likely to appear in February 2017. The seasonal component of O3 did not exhibit a change point, while the trend component showed a discontinuous rise with two change points, which were most likely to occur in July 2018 and May 2017. (2) The phase and coherence relationships of PM2.5 and O3 on meteorological factors varied across different time scales. Stable phase relationships were observed on both small- and large-time scales, whereas no stable phase relationship was formed on medium scales. On all-time scales, sunshine duration was the best single variable for explaining PM2.5 variations and precipitation was the best single variable explaining O3 variations. When compared to single meteorological factors, the combination of multiple meteorological factors significantly improved the ability to explain variations in PM2.5 and O3 on small-time scales. (3) Atmospheric teleconnection factors were important driving factors affecting the response relationships of PM2.5 and O3 on meteorological factors and they had greater impact on the relationship at medium-time scales compared to small- and large-time scales.

Estimate of near-surface NO2 concentrations in Fenwei Plain, China, based on TROPOMI data and random forest model

Nitrogen dioxide (NO2) concentration is a crucial indicator of ground-level air quality, and elevated concentrations can adversely affect human health and the atmospheric environment. In this study, we utilized Tropospheric Monitoring Instrument (TROPOMI) tropospheric NO2 vertical column density data (VCD) and multi-source geographic data to establish a random forest regression (RF) model that accurately estimates NO2 concentrations near the ground in the Fenwei Plain. The model addresses the inherent limitations of traditional ground-based monitoring and provides data support for analyzing regional pollution spatial and temporal characteristics. (1) The RF model based on TROPOMI and geographic data demonstrates high estimation accuracy, with monthly average RF model fit and validation coefficient of determination (R2) reaching 0.949 and 0.875, respectively. (2) A complex nonlinear relationship exists between near-surface NO2 concentration and multi-source geographic data. The RF model's estimations reveal clear seasonal and regional variations in near-surface NO2 concentration. Concentrations are generally highest in winter, followed by spring and autumn, and lowest in summer. The high NO2 concentrations are primarily mainly distributed in the plains and river valleys with low elevation and dense population density. The model estimation results also indicate that the estimated effect is better when the NO2 concentration fluctuates less and anthropogenic emission reduction measures significantly impact the NO2 concentration near the ground. (3) The population exposure risk results indicate that most cities in the Fenwei Plain face varying exposure risks. These findings offer valuable insights for regional NO2 pollution management.

Forecasting of AQI (PM2.5) for the three most polluted cities in India during COVID-19 by hybrid Daubechies discrete wavelet decomposition and autoregressive (Db-DWD-ARIMA) model

Air pollution has emerged as a significant environmental challenge at the global level, and India is majorly affected by it. Numerous emission sources, such as automobiles, industries, fuel-burning for household and commercial activities, and dust due to construction activities, are responsible for air pollution. The lockdown in India which was clamped for controlling the spread of virulent disease also brought down the level of pollutants in air significantly. The proposed approach deals with the application of the hybrid model of Daubechies discrete wavelet decomposition (Db-DWD) and the autoregressive integrated moving average (ARIMA) model for modeling and forecasting the chaotic data of air quality index (PM2.5) from the three most polluted cities (Agra, New Delhi, and Varanasi) in India for pre and within lockdown periods. The estimated outputs of the component series are then reconstructed to obtain the final forecast of the AQI data. The statistical evaluation compares the performance of the simple ARIMA model and the joint Db-DWD-ARIMA model. Also, the coupled model has been applied for forecasting efficacy with Daubechies mother wavelet of orders 5, 8, 10, and 12. The hybrid model reduced forecasting errors and improved accuracy significantly. Secondly, the forecasting efficiencies in this hybrid model have enhanced with the increase in wavelet order. This study will help to assess and take appropriate steps to control air pollution levels and to monitor the growing air pollutants, which will be significant for our existence.

Outdoor Air Pollution and Childhood Respiratory Disease: The Role of Oxidative Stress

The leading mechanisms through which air pollutants exert their damaging effects are the promotion of oxidative stress, the induction of an inflammatory response, and the deregulation of the immune system by reducing its ability to limit infectious agents' spreading. This influence starts in the prenatal age and continues during childhood, the most susceptible period of life, due to a lower efficiency of oxidative damage detoxification, a higher metabolic and breathing rate, and enhanced oxygen consumption per unit of body mass. Air pollution is involved in acute disorders like asthma exacerbations and upper and lower respiratory infections, including bronchiolitis, tuberculosis, and pneumoniae. Pollutants can also contribute to the onset of chronic asthma, and they can lead to a deficit in lung function and growth, long-term respiratory damage, and eventually chronic respiratory illness. Air pollution abatement policies, applied in the last decades, are contributing to mitigating air quality issues, but more efforts should be encouraged to improve acute childhood respiratory disease with possible positive long-term effects on lung function. This narrative review aims to summarize the most recent studies on the links between air pollution and childhood respiratory illness.

Multi-stage planning of LID-GREI urban drainage systems in response to land-use changes

Long-term planning of urban drainage systems aimed at maintaining the sustainability of urban hydrology remains challenging. In this study, an innovative multi-stage planning framework involving two adaptation pathways for optimizing hybrid low impact development and grey infrastructure (LID-GREI) layouts in opposing chronological orders was explored. The Forward Planning and Backward Planning are adaptation pathways to increase LID in chronological order based on the initial development stage of an urban built-up area and reduce LID in reverse chronological order based on the final development stage, respectively. Two resilience indicators, which considered potential risk scenarios of extreme storms and pipeline failures, were used to evaluate the performance of optimized layouts when land-use changed and evolved over time. Compared these two pathways, Forward Planning made the optimized layouts more economical and resilient in most risk scenarios when land-use changed, while the layouts optimized by Backward Planning showed higher resilience only in the initial stage. Furthermore, a decentralized scheme in Forward Planning was chosen as the optimal solution when taking costs, reliability, resilience, and land-use changes into an overall consideration. Nevertheless, this kind of reverse optimization order offers a novel exploration in planning pathways for discovering the alternative optimization schemes. More comprehensive solutions can be provided to decision-makers. The findings will shed a light on the exploration of optimized layouts in terms of spatial configuration and resilience performance in response to land-use changes. This framework can be used to support long-term investment and planning in urban drainage systems for sustainable stormwater management.

Investigating the association between air pollutants' concentration and meteorological parameters in a rapidly growing urban center of West Bengal, India: a statistical modeling-based approach

The ambient air quality in a city is heavily influenced by meteorological conditions. The city of Siliguri, known as the "Gateway of Northeast India", is a major hotspot of air pollution in the Indian state of West Bengal. Yet almost no research has been done on the possible impacts of meteorological factors on criterion air pollutants in this rapidly growing urban area. From March 2018 to September 2022, the present study aimed to determine the correlations between meteorological factors, including daily mean temperature (℃), relative humidity (%), rainfall (mm), wind speed (m/s) with the concentration of criterion air pollutants (PM_2.5, PM₁₀, NO₂, SO₂, CO, O₃, and NH₃). For this research, the trend of all air pollutants over time was also investigated. The Spearman correlation approach was used to correlate the concentration of air pollutants with the effect of meteorological variables on these pollutants. Comparing the multiple linear regression (MLR) and non-linear regression (MLNR) models permitted to examine the potential influence of meteorological factors on concentrations of air pollutants. According to the trend analysis, the concentration of NH3 in the air of Siliguri is rising, while the concentration of other pollutants is declining. Most pollutants showed a negative correlation with meteorological variables; however, the seasons impacted on how they responded. The comparative regression research results showed that although the linear and non-linear models performed well in predicting particulate matter concentrations, they performed poorly in predicting gaseous contaminants. When considering seasonal fluctuations and meteorological parameters, the results of this research will definitely help to increase the accuracy of air pollution forecasting near future.

Spatiotemporal characteristics of air pollution in Chengdu-Chongqing urban agglomeration (CCUA) in Southwest, China: 2015-2021

Studying the spatiotemporal characteristics of air pollutants in urban agglomerations and their response factors will help to improve the quality of urban living. In combining air quality monitoring data and wavelet analysis from the Chengdu-Chongqing urban agglomeration (CCUA), this study assessed the spatiotemporal distribution characteristics and influential factors of air pollutants on daily, monthly and annual scales. The results showed that the concentration of air pollutants in the CCUA has decreased year by year, and air quality has improved. Except for O₃, pollutants in autumn and winter were higher than those in summer. The spatial distribution of air pollutants was obvious distributed in Chengdu, Chongqing, Zigong and Dazhou. Pollution incidents were mainly concentrated in winter. The 6 air pollutants and air quality index (AQI) have dominant periods on multiple time scales. AQI showed positive coherence with PM_2.5 and PM₁₀ on multiple time scales, and obvious positive coherence with SO₂, CO, NO₂ and O₃ in the short term scale. AQI was not strongly correlated with the fire point, but exhibited obvious negative coherence in the long term scale. In addition, AQI showed an obvious positive correlation with temperature and sunshine hours in short term, and a clear negative correlation with humidity and rainfall. The research results of this paper will provide a reference for pollution prevention and control in the CCUA.

Modelling driving factors of PM2.5 concentrations in port cities of the Yangtze River Delta

PM_2.5 is one of the major air pollutants in port cities of the Yangtze River Delta (YRD) of China. Understanding the driving factors of PM_2.5 is essential to guide air pollution prevention and control. We selected 17 major port cities in YRD to study the driving factors of PM_2.5 in 2019 and 2020. Generalized Additive Models were built to model the non-linear effects of single, multiple and interactions of driving factors on the variations of PM_2.5. NO₂, SO₂ and the day of year are most strongly associated with the variation of PM_2.5 concentration when used alone. Anthropogenic emissions play complicated roles in regulating PM_2.5 concentration. Although the effect of cargo throughput (CT) on PM_2.5 concentration is non-monotonic, higher PM_2.5 levels are found to be associated with higher levels of SO₂ and CT. This work can potentially provide a scientific basis for formulating PM_2.5 prevention and control policies in the region.

Spatiotemporal Distribution Patterns and Exposure Risks of PM2.5 Pollution in China

The serious pollution of PM2.5 caused by rapid urbanization in recent years has become an urgent problem to be solved in China. Annual and daily satellite-derived PM2.5 datasets from 2001 to 2020 were used to analyze the temporal and spatial patterns of PM2.5 in China. The regional and population exposure risks of the nation and of urban agglomerations were evaluated by exceedance frequency and population weight. The results indicated that the PM2.5 concentrations of urban agglomerations decreased sharply from 2014 to 2020. The region with PM2.5 concentrations less than 35 μg·m−3 accounted for 80.27% in China, and the average PM2.5 concentrations in 8 urban agglomerations were less than 35 μg·m−3 in 2020. The spatial distribution pattern of PM2.5 concentrations in China revealed higher concentrations to the east of the Hu Line and lower concentrations to the west. The annual regional exposure risk (RER) in China was at a high level, with a national average of 0.75, while the average of 14 urban agglomerations was as high as 0.86. Among the 14 urban agglomerations, the average annual RER was the highest in the Shandong Peninsula (0.99) and lowest in the Northern Tianshan Mountains (0.76). The RER in China has obvious seasonality; the most serious was in winter, and the least serious was in summer. The population exposure risk (PER) east of the Hu Line was significantly higher than that west of the Hu Line. The average PER was the highest in Beijing-Tianjin-Hebei (4.09) and lowest in the Northern Tianshan Mountains (0.71). The analysis of air pollution patterns and exposure risks in China and urban agglomerations in this study could provide scientific guidance for cities seeking to alleviate air pollution and prevent residents’ exposure risks.

The Impact of Central Heating on the Urban Thermal Environment Based on Multi-Temporal Remote Sensing Images

Research on the impact of anthropogenic heat discharge in a thermal environment is significant in climate change research. Central heating is more common in the winter in Northeast China as an anthropogenic heat. This study investigates the impact of central heating on the thermal environment in Shenyang, Changchun, and Harbin based on multi-temporal land surface temperature retrieval from remote sensing. An equivalent heat island index method was proposed to overcome the problem of the method based on a single-phase image, which cannot evaluate all the central heating season changes. The method improves the comprehensiveness of a thermal environment evaluation by considering the long-term heat accumulation. The results indicated a significant increase in equivalent heat island areas at night with 22.1%, 17.3%, and 19.5% over Shenyang, Changchun, and Harbin. The increase was significantly positively correlated with the central heating supply (with an R-value of 0.89 for Shenyang, 0.93 for Changchun, and 0.86 for Harbin; p < 0.05). The impact of central heating has a more significant effect than the air temperature. The results provide a reference for future studies of urban thermal environment changes.

Modeling the Determinants of PM2.5 in China Considering the Localized Spatiotemporal Effects: A Multiscale Geographically Weighted Regression Method

Many studies have identified the influences of PM2.5. However, very little research has addressed the spatiotemporal dependence and heterogeneity in the relationships between impact factors and PM2.5. This study firstly utilizes spatial statistics and time series analysis to investigate the spatial and temporal dependence of PM2.5 at the city level in China using a three-year (2015–2017) dataset. Then, a new local regression model, multiscale geographically weighted regression (MGWR), is introduced, based on which we measure the influence of PM2.5. A spatiotemporal lag is constructed and included in MGWR to account for spatiotemporal dependence and spatial heterogeneity simultaneously. Results of MGWR are comprehensively compared with those of ordinary least square (OLS) and geographically weighted regression (GWR). Experimental results show that PM2.5 is autocorrelated in both space and time. Compared with existing approaches, MGWR with a spatiotemporal lag (MGWRL) achieves a higher goodness-of-fit and a more significant effect on eliminating residual spatial autocorrelation. Parameter estimates from MGWR demonstrate significant spatial heterogeneity, which traditional global models fail to detect. Results also indicate the use of MGWR for generating local spatiotemporal dependence evaluations which are conditioned on various covariates rather than being simple descriptions of a pattern. This study offers a more accurate method to model geographic events.

Spatial and dynamic effects of air pollution on under-five children's lower respiratory infections: an evidence from China 2006 to 2017

Air pollution has been a deeply concerned issue posing an immediate and profound threat to human's lower respiratory health in China. The health of children under 5 years old, regarded as a key index of public health progress in a country, is closely related to the long-term human capital development. Hence, it is vital to investigate the potential association between air pollution and children's lower respiratory health outcomes and to explore related policy implications regarding the public health and the pollution regulation. As air pollutants diffuse across adjacent regions rather easily, considering the spatial spillover effect is meaningful in course of acquiring the aforementioned association. Based on the proposed province-level panel dataset of China during 2006-2017, this study constructs a dynamic spatial panel Durbin model to investigate the impact of air pollution on under-five children's lower respiratory infections. As a result, (1) both air pollution and children's respiratory health have obvious spatial spillover effects, and the latter has an outstanding characteristic of path dependence in time. (2) In the short term, air pollution presents significant negative impact on children's respiratory health, while in the long run, the impact decreases dramatically. (3) Regional comparison indicates that children in the western China are the most susceptible to air pollution followed by children in the central and eastern regions. (4) Other control variables have significant and varying impacts both in the short and long term. Particularly, this paper proves the existence of "siphon effect" in children healthcare system in China. From a broader and more comprehensive perspective, this study provides effective and constructive basis for policy making, in favor of improving children's health under air pollution and promoting sustainable development in China.

Factors Influencing PM2.5 Concentrations in the Beijing–Tianjin–Hebei Urban Agglomeration Using a Geographical and Temporal Weighted Regression Model

Air pollution is the environmental issue of greatest concern in China, especially the PM2.5 pollution in the Beijing–Tianjin–Hebei urban agglomeration (BTHUA). Based on sustainable development, it is of interest to study the spatiotemporal distribution of PM2.5 and its influencing mechanisms. This study reveals the temporal evolution and spatial clustering characteristic of PM2.5 pollution from 2015 to 2019, and quantifies the drivers of its natural and socioeconomic factors on it by using a geographical temporal weighted regression model. Results show that PM2.5 concentrations reached their highest level in 2015 before decreasing in the following years. The monthly averages all present a U-shaped change trend. Relative to the traditional high concentrations in the northern part of the BTHUA domain in 2015, the gap in pollution between the north and south has reduced since 2018. The obvious spatial heterogeneity was demonstrated in both the strength and direction of the variables. This study may help identify reasons for high PM2.5 concentrations and suggest appropriate targeted control and prevention measures.

Meteorological Influences on Spatiotemporal Variation of PM2.5 Concentrations in Atmospheric Pollution Transmission Channel Cities of the Beijing–Tianjin–Hebei Region, China

Understanding the spatiotemporal characteristics of PM_2.5 concentrations and identifying their associated meteorological factors can provide useful insight for implementing air pollution interventions. In this study, we used daily air quality monitoring data for 28 air pollution transmission channel cities in the Beijing–Tianjin–Hebei region during 2014–2019 to quantify the relative contributions of meteorological factors on spatiotemporal variation in PM_2.5 concentration by combining time series and spatial perspectives. The results show that annual mean PM_2.5 concentration significantly decreased in 24 of the channel cities from 2014 to 2019, but they all still exceeded the Grade II Chinese Ambient Air Quality Standards (35 μg m⁻³) in 2019. PM_2.5 concentrations exhibited clear spatial agglomeration in the most polluted season, and their spatial pattern changed slightly over time. Meteorological variables accounted for 31.96% of the temporal variation in PM_2.5 concentration among the 28 cities during the study period, with minimum temperature and average relative humidity as the most critical factors. Spatially, atmospheric pressure and maximum temperature played a key role in the distribution of PM_2.5 concentration in spring and summer, whereas the effect of sunshine hours increased greatly in autumn and winter. These findings highlight the importance of future clean air policy making, but also provide a theoretical support for precise forecasting and prevention of PM_2.5 pollution.

Assessment of the Factors Influencing Sulfur Dioxide Emissions in Shandong, China

Sulfur dioxide (SO2) is a serious air pollutant emitted from different sources in many developing regions worldwide, where the contribution of different potential influencing factors remains unclear. Using Shandong, a typical industrial province in China as an example, we studied the spatial distribution of SO2 and used geographical detectors to explore its influencing factors. Based on the daily average concentration in Shandong Province from 2014 to 2019, we explored the influence of the diurnal temperature range, secondary production, precipitation, wind speed, soot emission, sunshine duration, and urbanization rate on the SO2 concentration. The results showed that the diurnal temperature range had the largest impact on SO2, with q values of 0.69, followed by secondary production (0.51), precipitation (0.46), and wind speed (0.42). There was no significant difference in the SO2 distribution between pairs of sunshine durations, soot emissions, and urbanization rates. The meteorological factors of precipitation, wind speed, and diurnal temperature range were sensitive to seasonal changes. There were nonlinear enhancement relationships among those meteorological factors to the SO2 pollution. There were obvious geographical differences in the human activity factors of soot emissions, secondary production, and urbanization rates. The amount of SO2 emissions should be adjusted in different seasons considering the varied effect of meteorological factors.

A full-coverage estimation of PM2.5 concentrations using a hybrid XGBoost-WD model and WRF-simulated meteorological fields in the Yangtze River Delta Urban Agglomeration, China

In spite of the state-of-the-art performances of machine learning in the PM_2.5 estimation, the high-value PM_2.5 underestimation and non-random aerosol optical depth (AOD) missing are still huge obstacles. By incorporating wavelet decomposition (WD) into the extreme gradient boosting (XGBoost), a hybrid XGBoost-WD model was established to obtain the full-coverage PM_2.5 estimation at 3-km spatial resolution in the Yangtze River Delta Urban Agglomeration (YRDUA). In this study, 3-km-resolution meteorological fields simulated by WRF along with AOD derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were served as explanatory variables. Model MW and Model NW were developed using XGBoost-WD for the areas with and without AOD respectively to obtain a full-coverage PM_2.5 mapping in the YRDUA. The XGBoost-WD model showed good performances in estimating PM_2.5 with R² of 0.80 in the Model MW and 0.87 in the Model NW. Moreover, the K-value of Model MW increased from 0.77 to 0.79 and that of Model NM increased from 0.81 to 0.86 compared with the model without the step of WD, indicating an improvement on the problem of PM_2.5 underestimation. Due to a better ability of capturing abrupt changes in the PM_2.5 concentrations, the spatial evolution of PM_2.5 during a typical pollution event could be mapped more accurately. Finally, the analysis of variable importance showed that the three most important variables in the estimation of the low-frequency coefficients of PM_2.5 (PM_2.5_A4) were temperature at 2 m (T2), day of year (DOY) and longitude (LON), while that in the high-frequency coefficients of PM_2.5 (PM_2.5_D) were CO, AOD and NO₂. This study not only provided an effective solution to the PM_2.5 underestimation and AOD missing problems in the PM_2.5 estimation, but also proposed a new method to further refine the sophisticated correlations between PM_2.5 and some spatiotemporal variables.

Persistent pollution episodes, transport pathways, and potential sources of air pollution during the heating season of 2016-2017 in Lanzhou, China

As one of the most important industrial cities in Northwest China, Lanzhou currently suffers from serious air pollution. This study analyzed the formation mechanism and potential source areas of persistent air pollution in Lanzhou during the heating period from November 1, 2016 to March 31, 2017 based on the air pollutant concentrations and relevant meteorological data. Our findings indicate that particulate pollution was extremely severe during the study period. The daily PM_2.5 and PM₁₀ concentrations had significantly negative correlations with daily temperature, wind speed, maximum daily boundary layer height, while the daily PM_2.5 and PM₁₀ concentrations showed significantly positive correlations with daily relative humidity. Five persistent pollution episodes were identified and classified as either stagnant accumulation or explosive growth types according to the mechanism of pollution formation and evolution. The PM_2.5 and PM₁₀ concentrations and PM_2.5/PM₁₀ ratio followed a growing "saw-tooth cycle" pattern during the stagnant accumulation type event. Dust storms caused abrupt peaks in PM₁₀ and a sharp decrease in the PM_2.5/PM₁₀ ratio in explosive growth type events. The potential sources of PM₁₀ were mainly distributed in the Kumtag Desert in Xinjiang Uygur Autonomous Region, the Qaidam Basin and Hehuang Valley in Qinghai Province, and the western and eastern Hexi Corridor in Gansu Province. The contributions to PM₁₀ were more than 120 μg/m³. The important potential sources of PM_2.5 were located in Hehuang Valley in Qinghai and Linxia Hui Autonomous Prefecture in Gansu; the concentrations of PM_2.5 were more than 60 μg/m³.

Does the "Blue Sky Defense War Policy" Paint the Sky Blue?-A Case Study of Beijing-Tianjin-Hebei Region, China

Improving air quality is an urgent task for the Beijing-Tianjin-Hebei (BTH) region in China. In 2018, utilizing 365 days' daily concentration data of six air pollutants (including PM_2.5, PM₁₀, SO₂, NO₂, CO and O₃) at 947 air quality grid monitoring points of 13 cities in the BTH region and controlling the meteorological factors, this paper takes the implementation of the Blue Sky Defense War (BSDW) policy as a quasi-natural experiment to examine the emission reduction effect of the policy in the BTH region by applying the difference-in-difference method. Results show that the policy leads to the significant reduction of the daily average concentration of PM_2.5, PM₁₀, SO₂, O₃ by -1.951 μg/m³, -3.872 μg/m³, -1.902 μg/m³, -7.882 μg/m³ and CO by -0.014 mg/m³, respectively. The results of the robustness test support the aforementioned conclusions. However, this paper finds that the concentration of NO₂ increases significantly (1.865 μg/m³). In winter heating seasons, the concentration of SO₂, CO and O₃ decrease but PM_2.5, PM₁₀ and NO₂ increase significantly. Besides, resource intensive cities, non-key environmental protection cities and cities in the north of the region have great potential for air pollutant emission reduction. Finally, policy suggestions are recommended; these include setting specific goals at the city level, incorporating more cities into the list of key environmental protection cities, refining the concrete indicators of domestic solid fuel, and encouraging and enforcing clean heating diffusion.

The influence of urban haze pollution on urban shrinkage in China-an analysis of the mediating effect of the labor supply

Panel data of 234 cities in China from 2011 to 2018 are used to measure the urban shrinkage index. PM_2.5 is used as an indicator of haze pollution, and labor supply is the mediator. On this basis, the influence mechanism of haze pollution on urban shrinkage is analyzed theoretically. Next, using the dynamic panel model and the mediating effect model, we empirically examine the impact of urban shrinkage on haze pollution and the mediating effect of labor supply. The main findings are as follows: haze pollution increases the degree of urban shrinkage. Labor supply plays a regulatory role in the process of haze pollution affecting urban shrinkage. The influence of haze pollution on labor supply is significantly negative, that is, haze pollution will result in a decline of the city labor supply. Every 1 percentage point increase in smog pollution will reduce the labor supply by 1.4585 percentage points. The effect of labor supply on urban contraction is significantly negative. According to our research, pertinent policies and suggestions are proposed to reduce both urban shrinkage and haze pollution.

Responses in PM2.5 and its chemical components to typical unfavorable meteorological events in the suburban area of Tianjin, China

Air pollution is the result of enormous emissions and unfavorable meteorological conditions. The role of meteorology, particularly extremely unfavorable meteorological events (EUMEs), in processing atmospheric PM_2.5 pollution has not been fully addressed. This work examined the variations of PM_2.5 mass and its chemical components associated with various meteorological parameters and three EUMEs based on meteorological observations and analysis combined with one-year long in situ measurement in 2018 in the suburban area of Tianjin, China. Analysis shows that the polluted days in 2018 were mostly related to the increase in sulfate, nitrate, and ammonium (SNA). Temperature between -2 to 13 °C is more favorable for the formation of SNA, while high temperature exceeding 28 °C is favorable for the formation of organic carbon and sulfate. Most of the ions and carbon components showed significant increase in concentrations when relative humidity exceeded 80%. The maximum decreasing rate of PM_2.5 concentrations due to increase in wind speed and planetary boundary height could be 15.35 μg m^-3 (m s^-1)^-1, and 34.37 μg m^-3 (100 m)^-1, respectively. EUMEs showed significant impacts on PM_2.5 components, in which PM_2.5 concentrations showed the most significant increase under temperature inversion (TI) events, and surface-based TI (SBTI) events usually have much stronger impacts on PM_2.5 concentrations than elevated TI (ELTI). Nitrate was found to be the most sensitive component to EUMEs, especially under multiple EUMEs. The synthetic effects of multiple EUMEs could result in an increase of nitrate by 35.53 μg m^-3 (523.3%). In addition, OC and sulfate are more sensitive to heat wave events. Our analysis provides improved understanding of the formation of PM_2.5 pollution with respect to meteorology, particularly EUMEs. Based on such information, more attention may be needed on the collaborative prediction of EUMEs and air pollution episodes.

Evaluating the influence of land use and land cover change on fine particulate matter

Fine particulate matter (i.e. particles with diameters smaller than 2.5 microns, PM_2.5) has become a critical environmental issue in China. Land use and land cover (LULC) is recognized as one of the most important influence factors, however very fewer investigations have focused on the impact of LULC on PM_2.5. The influences of different LULC types and different land use and land cover change (LULCC) types on PM_2.5 are discussed. A geographically weighted regression model is used for the general analysis, and a spatial analysis method based on the geographic information system is used for a detailed analysis. The results show that LULCC has a stable influence on PM_2.5 concentration. For different LULC types, construction lands have the highest PM_2.5 concentration and woodlands have the lowest. The order of PM_2.5 concentration for the different LULC types is: construction lands > unused lands > water > farmlands >grasslands > woodlands. For different LULCC types, when high-grade land types are converted to low-grade types, the PM_2.5 concentration decreases; otherwise, the PM_2.5 concentration increases. The result of this study can provide a decision basis for regional environmental protection and regional ecological security agencies.

The spatial effect of fiscal decentralization on haze pollution in China

The fiscal decentralization system under China's political centralization affects local economic and environmental policies, and thus has an important impact on environmental quality. This paper uses the panel data of 285 cities in China from 2003 to 2018 and the spatial Durbin model to empirically analyze the impact of fiscal decentralization on haze pollution and its mechanism. The results show that the increase in fiscal decentralization will significantly aggravate the haze pollution in and around the region, and this conclusion is still valid after a series of robustness tests. Moreover, the impact of fiscal decentralization on haze pollution has significant heterogeneity in the size and region of the city, and the sample period. In addition, mechanism analyses show that fiscal decentralization has aggravated haze pollution by increasing infrastructure construction, reducing environmental regulations, and intensifying market segmentation. Further analyses reveal that, on the one hand, local governments have the ability to control haze pollution in their own regions according to their own wishes and interests, but on the other hand, adjustments to environmental policies in surrounding areas will significantly inhibit the control of environmental policies in the region, thereby making local governments haze pollution has not been effectively controlled. This is essentially a "Race to bottom" phenomenon among local governments in environmental policies.

Near-road air quality modelling that incorporates input variability and model uncertainty

Dispersion modelling is an effective tool to estimate traffic-related fine particulate matter (PM_2.5) concentrations in near-road environments. However, many sources of uncertainty and variability are associated with the process of near-road dispersion modelling, which renders a single-number estimate of concentration a poor indicator of near-road air quality. In this study, we propose an integrated traffic-emission-dispersion modelling chain that incorporates several major sources of uncertainty. Our approach generates PM_2.5 probability distributions capturing the uncertainty in emissions and meteorological conditions. Traffic PM_2.5 emissions from 7 a.m. to 6 p.m. were estimated at 3400 ± 117 g. Modelled PM_2.5 levels were validated against measurements along a major arterial road in Toronto, Canada. We observe large overlapping areas between modelled and measured PM_2.5 distributions at all locations along the road, indicating a high likelihood that the model can reproduce measured concentrations. A policy scenario expressing the impact of reductions in truck emissions revealed that a 30% reduction in near-road PM_2.5 concentrations can be achieved by upgrading close to 55% of the current trucks circulating along the corridor. A speed limit reduction of 10 km/h could lead to statistically significant increases in PM_2.5 concentrations at twelve out of the eighteen locations.

Modification of the effects of nitrogen dioxide and sulfur dioxide on congenital limb defects by meteorological conditions

STUDY QUESTION

Can meteorological conditions modify the associations between NO2 and SO2 exposure and congenital limb defects (CLDs) during the first trimester of pregnancy?

SUMMARY ANSWER

Increases in NO2 and SO2 exposure were consistently associated with higher risks of CLDs during the first trimester of pregnancy; both low- and high-temperature exposure and high air humidity act synergistically with the two air pollutants on CLDs.

WHAT IS KNOWN ALREADY

Animal studies have indicated air pollutants are associated with CLDs, but corresponding epidemiological studies are limited with equivocal conclusions. Meteorological conditions are closely connected to the generation, diffusion, distribution and even chemical toxicity of air pollutants.

STUDY DESIGN, SIZE, DURATION

This case-control study included 972 cases of CLDs and 9720 controls in Changsha, China during 2015-2018.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Cases from the hospital based monitoring system for birth defects (including polydactyly, syndactyly, limb shortening, and clubfoot) and healthy controls from the electronic medical records system were studied. Complete data on daily average NO2 and SO2 concentrations and meteorological variables were obtained from local monitoring stations to estimate monthly individual exposures during the first trimester of pregnancy, using the nearest monitoring station approach for NO2 and SO2 concentrations, and the city-wide average approach for temperature and relative humidity, respectively. The 25th and 75th percentiles of daily mean temperature, as well as the 50th percentile of daily mean relative humidity during the study period were used to classify high- and low-temperature exposure, and high humidity exposure based on existing evidence and local climate characteristics. Multivariate logistic regression models were used to estimate the independent effects per 10 μg/m3 increase in NO2 and SO2 on CLDs, and the attribute proportions of interaction (API) were used to quantify the additive joint effects of air pollutants with meteorological conditions after including a cross product interaction term in the regression models.

MAIN RESULTS AND THE ROLE OF CHANCE

NO2 and SO2 exposures during the first trimester of pregnancy were consistently and positively associated with overall CLDs and subtypes, with adjusted odd ratios (aORs) ranging from 1.13 to 1.27 for NO2, and from 1.37 to 2.49 for SO2. The effect estimates were generally observed to be the strongest in the first month and then attenuated in the second and third months of pregnancy. Synergistic effects of both low and high temperature in combination with NO2 (with APIs ranging from 0.07 to 0.38) and SO2 (with APIs ranging from 0.18 to 0.51) appeared in the first trimester of pregnancy. Several significant modifying effects by high humidity were also observed, especially for SO2 (with APIs ranging from 0.13 to 0.38). Neither NO2 nor SO2 showed an interactive effect with season of conception.

LIMITATIONS, REASONS FOR CAUTION

The methods used to estimate individual exposure levels of air pollutants and meteorological factors may lead to the misclassification bias because of the lack of information on maternal activity patterns and residential mobility during pregnancy. Moreover, we were unable to consider several potentially confounding factors, including socioeconomic status, maternal nutrient levels, alcohol use and smoking during early pregnancy due to unavailable data, although previous studies have suggested limited change to the results after when including these factors in the analysis.

WIDER IMPLICATIONS OF THE FINDINGS

The findings are helpful for understanding the combined effects of air pollution and meteorological conditions on birth defects. Environmental policies and practices should be formulated and implemented to decrease air pollutant emissions and improve meteorological conditions to reduce their harmful effects on pregnancy. Additionally, pregnant women should be suggested to reduce outdoor time when the air quality is poor, especially when ambient temperature is higher or lower than what is comfortable, or when it is excessively humid.

STUDY FUNDING/COMPETING INTEREST(S)

The study is funded by Major Scientific and Technological Projects for Collaborative Prevention and Control of Birth Defects in Hunan Province (2019SK1012), Major Research and Development Projects in Hunan Province (2018SK2060) and Scientific and Technological Department Projects in Hunan Province (2017SK50802). There are no competing interests.

TRIAL REGISTRATION NUMBER

N/A.

Independent and interactive effects of air pollutants and ambient heat exposure on congenital heart defects

Accumulating studies have been focused on the independent effects of air pollutants and ambient heat exposure on congenital heart defects (CHDs) but with inconsistent results, and their interactive effect remains unclear. A case-control study including 921 cases and 9210 controls was conducted in Changsha, China in warm season in 2015-2018. The gravidas were assigned monthly averages of daily air pollutants and daily maximum temperature using the nearest monitoring station method and city-wide average method, respectively, during the first trimester of pregnancy. Multivariate logistic regression models were used to estimate the independent effects of each air pollutant and different ambient heat exposure indicators. Their additive joint effects were quantified using attribute proportions of interaction (API). Increasing SO₂ consistently increased the risk of CHDs in the first trimester of pregnancy, with aORs ranging from 1.78 to 2.04. CO, NO₂ and PM_2.5 exposure in the first month of pregnancy, and O₃ exposure in the second and third month of pregnancy were also associated with elevated risks of CHDs, with aORs ranging from 1.04 to 1.15. Depending on the ambient heat exposure indicator used, air pollutants showed more apparent synergistic effects (API > 0) with less and moderately intense heat exposure. Maternal exposure to CO, NO₂, SO₂, PM_2.5 and O₃ during early pregnancy increased risk of CHDs, and ambient heat exposure may enhance these effects. Our findings help to understand the interactive effect of air pollution with ambient heat exposure on CHDs, which is of vital public health significance.

Exposure interval to ambient fine particulate matter (PM2.5) collected in Southwest China induced pulmonary damage through the Janus tyrosine protein kinase-2/signal transducer and activator of transcription-3 signaling pathway both in vivo and in vitro

PM2.5 is a well-known air pollutant threatening public health. Studies confirmed that exposure to the particles could impair pulmonary function, cause chronic obstructive pulmonary disease, and increase the incidence of lung cancer. The characteristic of PM2.5 varies across regions. The toxic function of PM2.5 in southwest China remains to be elucidated. This study aimed to investigate lung injury and its mechanisms induced by PM2.5 collected in Chengdu. Rats were administered with PM2.5 by intratracheal instillation for 4 weeks. Biochemical, cell count, and inflammation-related parameters were measured. Lung tissues were obtained for hematoxylin and eosin and Masson's trichrome staining. The expression levels of vascular endothelial growth factor (VEGF), Janus tyrosine protein kinase-2 (JAK-2), and signal transducer and activator of transcription-3 (STAT-3) were detected by immunohistochemistry assays. Meanwhile, A549 cells were treated with the PM2.5. The cell cycle, and apoptosis were measured by flow cytometry. mRNA and protein expressions of JAK-2, STAT-3, p-STAT-3, and VEGFA were detected using qPCR and Western blot analysis respectively. Results of in vivo study showed that PM2.5 induced lung pathological injury, aggravated the accumulation of inflammatory cells, and increased the serum levels of inflammatory factors. In vitro experiments showed that PM2.5 disrupted the cell growth cycle and increased cell apoptosis through the activation of the JAK-2/STAT-3 signaling pathway. Taken together, this study provided convincing experimental evidence that PM2.5 collected in southwest China could induce pulmonary injury as manifested by inflammatory response and lung fibrosis, possibly through the modulation of the JAK-2/STAT-3 signaling pathway.

How Does Local Real Estate Investment Influence Neighborhood PM2.5 Concentrations? A Spatial Econometric Analysis

Real estate investment has been an important driving force in China’s economic growth in recent years, and the relationship between real estate investment and PM2.5 concentrations has been attracting widespread attention. Based on spatial econometric modelling, this paper explores the relationships between real estate investment and PM2.5 concentrations using multi-source panel data from 30 provinces in China between 1987 and 2017. The results demonstrate that compared with static spatial panel modelling, using a dynamic spatial Durbin lag model (DSDLM) more accurately reflects the influences of real estate investment on PM2.5 concentrations in China, and that PM2.5 concentrations show significant superposition effects and spillover effects. Moreover, there is an inverted U-shaped relationship between real estate investment and PM2.5 concentrations in the Eastern and Central Regions of China. At the national level, the impacts of real estate investment on land urbanization and PM2.5 concentrations first increased and then decreased over time. The key implications of this analysis are as follows. (1) it highlights the need for a unified PM2.5 monitoring platform among Chinese regions; (2) the quality of population urbanization rather than land urbanization should be given more attention; and (3) the speed of construction of green cities and building of green transportation systems and green town systems should be increased.

Biological effect of PM10 on airway epithelium-focus on obstructive lung diseases

Recently, a continuous increase in environmental pollution has been observed. Despite wide-scale efforts to reduce air pollutant emissions, the problem is still relevant. Exposure to elevated levels of airborne particles increased the incidence of respiratory diseases. PM₁₀ constitute the largest fraction of air pollutants, containing particles with a diameter of less than 10 μm, metals, pollens, mineral dust and remnant material from anthropogenic activity. The natural airway defensive mechanisms against inhaled material, such as mucus layer, ciliary clearance and macrophage phagocytic activity, may be insufficient for proper respiratory function. The epithelium layer can be disrupted by ongoing oxidative stress and inflammatory processes induced by exposure to large amounts of inhaled particles as well as promote the development and exacerbation of obstructive lung diseases. This review draws attention to the current state of knowledge about the physical features of PM₁₀ and its impact on airway epithelial cells, and obstructive pulmonary diseases.

Characterization and source apportionment of carbonaceous aerosols in fine particles at urban and suburban atmospheres of Ankara, Turkey

In order to find the spatial distribution characteristics of elemental (EC) and organic (OC) carbon in fine particles, daily PM_2.5 aerosol samples were collected at two different stations, between July 2014 and September 2015 in Ankara, Turkey. Concentrations of OC ranged from 2.1 to 42 μg m^-3 at urban station. These concentrations were higher than those obtained for suburban station whose values ranged from 1.3 to 15 μg m^-3. Concentrations of EC ranged from 0.7 to 4.9 μg m^-3 at the urban station. As in OC case, the corresponding levels were higher than those measured for suburban station. The associated EC levels ranged from 0.1 to 3.4 μg m^-3 for the suburban station. Daily changes in the levels of EC were larger than the OC levels. OC/EC ratios were lower with lower monthly variability in summer and higher with lower monthly variability in winter at the urban site. Medium and weak correlations were obtained between EC and OC in the winter and summer seasons, respectively, at both stations. Secondary organic carbon (SOC) was an important component of OC in PM_2.5 at the urban and suburban sites. The winter SOC level was higher than the summer SOC level at the urban site but slightly lower than the summer SOC level at the suburban site. Total carbon was apportioned using factor analysis for the eight carbon fraction data (OC1, OC2, OC3, OC4, EC1, EC2, EC3, and OP). The main sources of pollutants in the urban and suburban settings were from vehicular emissions, biomass and coal combustions, and road dust.

Vertical distributions of tropospheric SO2 based on MAX-DOAS observations: Investigating the impacts of regional transport at different heights in the boundary layer

Information on the vertical distribution of air pollutants is essential for understanding their spatiotemporal evolution underlying urban atmospheric environment. This paper presents the SO₂ profiles based on ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements from March 2018 to February 2019 in Hefei, East China. SO₂ decrease rapidly with increasing heights in the warm season, while lifted layers were observed in the cold season, indicating accumulation or long-range transport of SO₂ in different seasons might occur at different heights. The diurnal variations of SO₂ were roughly consistent for all four seasons, exhibiting the minimum at noon and higher values in the morning and late afternoon. Lifted layers of SO₂ were observed in the morning for fall and winter, implying the accumulation or transport of SO₂ in the morning mainly occurred at the top of the boundary layer. The bivariate polar plots showed that weighted SO₂ concentrations in the lower altitude were weakly dependent on wind, but in the middle and upper altitudes, higher weighted SO₂ concentrations were observed under conditions of middle-high wind speed. Concentration weighted trajectory (CWT) analysis suggested that potential sources of SO₂ in spring and summer were local and transported mainly occurred in the lower altitude from southern and eastern areas; while in fall and winter, SO₂ concentrations were deeply affected by long-range transport from northwestern and northern polluted regions in the middle and upper altitudes. Our findings provide new insight into the impacts of regional transport at different heights in the boundary layer on SO₂ pollution.

Characteristics and sources of atmospheric pollutants in typical inland cities in arid regions of central Asia: A case study of Urumqi city

The arid zone of central Asia secluded inland and has the typical features of the atmosphere. Human activities have had a significant impact on the air quality in this region. Urumqi is a key city in the core area of the Silk Road and an important economic center in Northwestern China. The urban environment is playing an increasingly important role in regional development. To study the characteristics and influencing factors of the main atmospheric pollutants in Urumqi, this study selected Urumqi's daily air quality index (AQI) data and observation data of six major pollutants including fine particulate matter (PM2.5), breathable particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3_8h) from 2014 to 2018 in conjunction with meteorological data to use a backward trajectory analysis method to study the main characteristics of atmospheric pollutants and their sources in Urumqi from 2014 to 2018. The results showed that: (1) From 2014 to 2018, the annual average of PM2.5, PM10, SO2, NO2 and CO concentrations showed a downward trend, and O3_8h concentrations first increased, then decreased, and then increased, reaching the highest value in 2018 (82.15 μg·m-3); The seasonal changes of PM2.5, PM10, SO2, NO2 and CO concentrations were characterized by low values in summer and fall seasons and high values in winter and spring seasons. The concentration of O3_8h, however, was in the opposite trend, showing the high values in summer and fall seasons, and low values in winter and spring seasons. From 2014 to 2018, with the exception of O3_8h, the concentration changes of the other five major air pollutants were high in December, January, and February, and low in May, June, and July; the daily changes showed a "U-shaped" change during the year. The high-value areas of the "U-shaped" mode formed around the 50th day and the 350th day. (2) The high-value area of AQI was from the end of fall (November) to the beginning of the following spring (March), and the low-value area was from April to October. It showed a U-shaped change trend during the year and the value was mainly distributed between 50 and 100. (3) The concentrations of major air pollutants in Urumqi were significantly negatively correlated with precipitation, temperature, and humidity (P<0.01), and had the highest correlation coefficients with temperature. (4) Based on the above analysis results, this study analyzed two severe pollution events from late November to early December. Analysis showed that the PM2.5/PM10 ratio in two events remained at about 0.1 when the pollution occurred, but was higher before and after the pollution (up to 1.46). It was shown that the pollution was a simple sandstorm process. Backward trajectory analysis clustered the airflow trajectories reaching Urumqi into 4 categories, and the trajectories from central Asia contributed the maximum values of average PM2.5 and PM10 concentrations.

Evaluation and Influencing Factors of Industrial Pollution in Jilin Restricted Development Zone: A Spatial Econometric Analysis

Winning the battle against pollution and strengthening ecological protection in all respects are vital for promoting green development and building a moderately prosperous ecological civilization in China. Using the entropy weight method, this paper establishes and evaluates a comprehensive industrial pollution index that contains and synthesizes six major industrial pollutants (wastewater, COD, waste gas, SO2, NOx, and solid waste) in the 2006–2015 period. Subsequently, this paper studies the spatiotemporal characteristics and influencing factors of industrial pollution via the Moran index and spatial econometric analysis. The empirical results indicate that (1) the temporal evolution of the industrial pollution index is characterized by an overall trend of first decreasing and then increasing. (2) The industrial pollution index of each county has certain geographical disparities and significant spatially polarized characteristics in 2006, 2009, 2012, and 2015. (3) The Moran test shows that there is a relatively significant spatial autocorrelation of the industrial pollution index among counties and that the geographical distribution of the industrial pollution index tends to show clustering. (4) Spatial regression models that incorporate spatial factors better explain the influencing factors of industrial pollution. The economic development level, technological progress, and industrialization are negatively correlated with industrial pollution, while population density and industrial production capacity are positively correlated. (5) Consequently, as relevant policy recommendations, this paper proposes that environmental cooperation linkage mechanisms, environmental protection credit systems, and green technology innovation systems should be established in different geographical locations to achieve the goals of green county construction and sustainable development.

A novel method for identifying hotspots and forecasting air quality through an adaptive utilization of spatio-temporal information of multiple factors

Air pollution exerts serious impacts on human health and sustainable development. The accurate forecasting of air quality can guide the formulation of mitigation strategies and reduce exposure to air pollution. It is beneficial to explicitly consider both spatial and temporal information of multiple factors, e.g., the meteorological data, in the forecasting of air pollutant concentrations. The temporal information of relevant factors collected at a location should be considered for forecasting. In addition, these factors recorded at other locations may also provide useful information. Existing methods utilizing the spatio-temporal information of these relevant factors are usually based on some very complicated frameworks. In this study, we propose a novel and simple spatial attention-based long short-term memory (SA-LSTM) that combines LSTM and a spatial attention mechanism to adaptively utilize the spatio-temporal information of multiple factors for forecasting air pollutant concentrations. Specifically, the SA-LSTM employs gated recurrent connections to extract temporal information of multiple factors at individual locations, and the spatial attention mechanism to spatially fuse the temporal information extracted at these locations. This method is effective and applicable to forecast any air pollutant concentrations when spatio-temporal information of relevant factors has to be utilized. To validate the effectiveness of the proposed SA-LSTM, we apply it to forecast the daily air quality in Hong Kong, a high density city with peculiar cityscapes, by using the air quality and meteorological data. Empirical results demonstrate that the proposed SA-LSTM outperforms the conventional models with respect to one-day forecast accuracy, especially for extreme values. Moreover, the attention weights learned by the SA-LSTM can identify hotspots of the air pollution process for reducing computational complexity of forecasting and provide a better understanding of the underlying mechanism of air pollution.

Characterizing variability and predictability for air pollutants with stochastic models

We investigate the dynamics of particulate matter, nitrogen oxides, and ozone concentrations in Hong Kong. Using fluctuation functions as a measure for their variability, we develop several simple data models and test their predictive power. We discuss two relevant dynamical properties, namely, the scaling of fluctuations, which is associated with long memory, and the deviations from the Gaussian distribution. While the scaling of fluctuations can be shown to be an artifact of a relatively regular seasonal cycle, the process does not follow a normal distribution even when corrected for correlations and non-stationarity due to random (Poissonian) spikes. We compare predictability and other fitted model parameters between stations and pollutants.

Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China’s urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

Capturing urban heat island formation in a subtropical city of China based on Landsat images: implications for sustainable urban development

Land use/cover change is the main driving force of urban expansion which influences human-environment interactions. Generally, the formation of urban heat islands (UHIs) can be referred to as a negative "by-product" of urbanization. In the context of rapid urbanization, the present paper aims to capture the landscape changes and three patterns of urban expansion (i.e., infill, extension, and leapfrog), and provide a better understanding of the formation of the surface urban heat island (SUHI) in Dongguan, China, during the past 20+ years. Urban land increased from 28.87 × 10³ ha in 1994 to 78.89 × 10³ ha in 2005 and 101.05 × 10³ ha in 2015, with a compound annual urban growth rate of 9.57% (1994-2005) and 2.51% (2005-2015), respectively. Based on the mean land surface temperature difference (Δ mean LST) between urban land (UL) and green space (GS), the SUHI intensity (SUHII) increased from 1.46 °C in 1994 to 2.32 °C in 2005 and 3.83 °C in 2015 in Dongguan. Overall, the Δ mean LST of urban areas increased from 2.61 °C (1994-2005) to 4.78 °C (2005-2015). The Δ mean LST between the city center and its surrounding areas decreased from 1994 to 2015, and the Δ mean LST between the city center and the suburbs gradually increased, primarily in 2015. In particular, both dense urban and the infill pattern of urban expansion had high mean LSTs in Dongguan, thus having negative impacts on sustainable urban development. The limited green space and open land should be strictly controlled or prohibited for transformation in urban areas. Particularly in dense regions, green roofs, green areas, and urban renewal actions could be considered for mitigating the urban heat island effect.

Modeling the spatio-temporal dynamics of air pollution index based on spatial Markov chain model

An environmental problem which is of concern across the globe nowadays is air pollution. The extent of air pollution is often studied based on data on the observed level of air pollution. Although the analysis of air pollution data that is available in the literature is numerous, studies on the dynamics of air pollution with the allowance for spatial interaction effects through the use of the Markov chain model are very limited. Accordingly, this study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data. This SMC model is pertinent to be applied since the daily data of API from 2012 to 2014 that have been gathered from 37 different air quality stations in Peninsular Malaysia is found to exhibit the property of spatial autocorrelation. Based on the spatial transition probability matrices found from the SMC model, specific characteristics of air pollution are studied in the regional context. These characteristics are the long-run proportion and the mean first passage time for each state of air pollution. It is found that the probability for a particular station's state to remain good is 0.814 if its neighbors are in a good state of air pollution and 0.7082 if its neighbors are in a moderate state. For a particular station having neighbors in a good state of air pollution, the proportion of time for it to continue being in a good state is 0.6. This proportion reduces to 0.4, 0.01, and 0 for the cell of moderate, unhealthy, and very unhealthy states, respectively. In addition, there exists a significant spatial dependence of API, indicating that air pollution for a particular station is dependent on the states of the neighboring stations.

Effects of socioeconomic and natural factors on air pollution in China: A spatial panel data analysis

China's energy use has increased significantly in recent years with the country's rapid economic growth and large-scale urbanization. Therefore, air pollution has become a major issue. In this study, we conducted spatial autocorrelation and spatial panel regression analyses of sulfur dioxide (SO₂) and nitrogen oxide (NO_X) emissions using the panel data of 31 provincial-level administrative units in China during the period 2011-2017 to comprehensively understand the factors affecting air pollutant emissions. This study contributes to the literature by considering comprehensive factors and spatial effects in the panel-data econometric framework of the whole country of China. The analysis of spatial characteristics shows that during the study period, pollutant emissions in China declined, although emissions in northern regions were still relatively high. Furthermore, SO₂ and NO_X emissions showed significant positive spatial autocorrelations. The results of a fixed-effect spatial lag model showed that both socioeconomic and natural factors were statistically significant for air pollutant emissions, although the degree differed by the type of pollutant. The population, the urbanization rate, the share of added value of secondary industry, and heating and cooling degree days positively affected emissions, while population density, per-capita gross regional product, precipitation, and relative humidity negatively affected emissions. Based on these results, we have put forward suggestions to address the issue of air pollution and achieve environmental sustainability, such as the promotion of regional cooperation and a transition of the economic structure.

PM2.5 and O3 pollution during 2015-2019 over 367 Chinese cities: Spatiotemporal variations, meteorological and topographical impacts

The strict Clean Air Action Plan has been in place by central and local government in China since 2013 to alleviate haze pollution. In response to implementation of the Plan, daytime PM_2.5 (particulate matter with aerodynamic diameter less than 2.5 μm) showed significant downward trends from 2015 to 2019, with the largest reduction during spring and winter in the North China Plain. Unlike PM_2.5, O₃ (ozone) showed a general increasing trend, reaching 29.7 μg m^-3 on summer afternoons. Increased O₃ and reduced PM_2.5 simultaneously occurred in more than half of Chinese cities, increasing to approximately three-fourths in summer. Declining trends in both PM_2.5 and O₃ occurred in only a few cities, varying from 19.1% of cities in summer to 33.7% in fall. Meteorological variables helped to decrease PM_2.5 and O₃ in some cities and increase PM_2.5 and O₃ in others, which is closely related to terrain. High wind speed and 24 h changing pressure favored PM_2.5 dispersion and dilution, especially in winter in southern China. However, O₃ was mainly affected by 24 h maximum temperature over most cities. Soil temperature was found to be a key factor modulating air pollution. Its impact on PM_2.5 concentrations depended largely on soil depth and seasons; spring and fall soil temperature at 80 cm below the surface had largely negative impacts. Compared with PM_2.5, O₃ was more significantly affected by soil temperature, with the largest impact at 20 cm below the surface and with less seasonal variation.

Interaction of Air Pollutants and Meteorological Factors on Birth Weight in Shenzhen, China

BACKGROUND

This study aimed to assess if air pollutants and meteorological factors synergistically affect birth outcomes in Shenzhen, China.

METHODS

A total of 1,206,158 singleton live births between 2005 and 2012 were identified from a birth registry database. Daily average measurements of particulate matter ≤10 µm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ambient air temperature (T), and dew point temperature (Td), a marker of humidity, were collected. Multivariable logistic regression models were used to evaluate associations between air pollution and small for gestational age (SGA), and full-term low birth weight (TLBW). We classified births into those conceived in the warm (May-October) and cold seasons (November-April) and then estimated interactions between air pollutants and meteorological factors.

RESULTS

An interquartile range (IQR) increase in PM10 exposure during the first trimester (23.1 µg/m) and NO2 during both the first and second trimesters (15.1 and 13.4 µg/m) was associated with SGA and TLBW risk; odds ratios ranged from 1.01 (95% confidence interval [CI] = 1.00, 1.02) to 1.09 (1.07, 1.12). We observed interactive effects of both air temperature and humidity on PM10 and SGA for newborns conceived in the warm season. Each IQR increase in PM10 (11.1 µg/m) increased SGA risk by 90% (95% CI = 19%, 205%), 29% (23, 34%), 61% (10, 38%), and 26% (21, 32%) when T < 5th percentile, 5th < T < 95th percentile, Td < 5th percentile, and 5th < Td < 95th percentile, respectively.

CONCLUSIONS

Our study found evidence of an interactive effect of air temperature and humidity on the relationship between PM10 exposure and SGA among newborns conceived in the warm season (May-October). Relatively low air temperature or humidity exacerbated the effects of PM10.

Heterogeneity of influential factors across the entire air quality spectrum in Chinese cities: A spatial quantile regression analysis

Most of the previous researches estimate influencing factors impact on air quality average without considering the heterogeneity of influential factors on different levels of air quality. In order to detect the different effects of influencing factors on air quality index (AQI) between lower-AQI and higher-AQI cities, this study applies a spatial quantile regression model (SQRM) to investigate heterogeneity of influential factors on AQI, while accounting for spatial autocorrelation of AQI. The results show that heterogeneity effects of windspeed, terrain slope, urbanization sprawl and spatial autocorrelation on AQI are large across the entire AQI spectrum, while heterogeneity effects of precipitation, temperature, relative humidity, terrain fluctuation and urbanization intensity on AQI are not obvious. The spatial positive autocorrelation of AQI in higher-AQI cities is greater than that in lower-AQI cities. Compared with higher-AQI cities, the negative impact of terrain slope on AQI is lager in lower-AQI cities. One unit increase in wind speed contributes AQI to decrease 9.31 to 5.64 then to 5.39 for lower, medium and higher-AQI cities. One unit increase in urbanization sprawl would lead AQI increase 25.6 to 15.6 then to 10.5 for lower, medium and higher-AQI cities. The heterogeneity analysis of meteorological, topographic and socioeconomic factors effects on air quality are of guiding significance for realizing the differentiation of policy measures for air pollution prevention and control.

Spatiotemporal characteristics of PM2.5 concentration in the Yangtze River Delta urban agglomeration, China on the application of big data and wavelet analysis

PM_2.5 pollution has been one of the main environmental issues of concern for the Yangtze River Delta Urban Agglomeration (YRDUA) during the recent decade. In this paper, allied with big data and wavelet analysis, spatiotemporal variations of PM_2.5 and its influencing factors (air pollutants and meteorological factors) are studied based on hourly concentrations of PM_2.5 from 2015 to 2018 in the YRDUA. Results showed that PM_2.5 presented a step-shaped decline from northwest to southeast in space and significant multi-scale temporal variations in time. On the macroscopic level, PM_2.5 concentrations decreased from 2015 to 2018, showing a U-shaped pattern within a year. On the microscopic level, it had a four-stage annual variation (January to March, April to June, July to September, October to December) and the mutation events mainly occurred in winter. There were two dominant periods of PM_2.5, an annual cycle on the time scale of 250-480 d and a semi-annual cycle on the time scale of 130-220 d. In addition, PM_2.5 showed time scale-dependent correlations with air pollutants and meteorological factors. Among air pollutants, the correlation between PM_2.5 and CO was the most consistent, and the correlation between PM_2.5 and SO₂/NO₂ improved with the increase of time scale, while the correlation between PM_2.5 and O₃ was positive at shorter time scales but negative at broader time scales. Among meteorological factors, the correlations between PM_2.5 and wind speed, precipitation, temperature, air pressure and relative humidity were mainly reflected at broader time scales. These findings would be helpful to improve the accuracy of prediction model and provide references for the ongoing joint prevention and control.

Analysis of the variability of airborne particulate matter with prevailing meteorological conditions across a semi-urban environment using a network of low-cost air quality sensors

The concentrations of fine and coarse fractions of airborne particulate matter (PM) and meteorological variables (wind speed, wind direction, temperature and relative humidity) were measured at six selected locations in Ile Ife, a prominent university town in Nigeria using a network of low-cost air quality (AQ) sensor units. The objective of the deployment was to collate baseline air quality data and assess the impact of prevailing meteorological conditions on PM concentrations in selected residential communities downwind of an iron smelting facility. The raw data obtained from OPC-N2 of the AQ sensor units was corrected using the RH correction factor developed based k-Kohler theory. This PM (corrected) fast time resolution data (20 s) from the AQ sensor units were used to create daily averages. The overall mean mass concentrations for PM_2.5 and PM₁₀ were 213.3, 44.1, 23.8, 27.7, 20.2 and 41.5 μg/m³ and; 439.9, 107.1, 55.0, 72.4, 45.5 and 112.0 μg/m³ for Fasina (Iron-Steel Smelting Factory, ISSF), Modomo, Eleweran, Fire Service, O.A.U. staff quarters and Obafemi Awolowo University Teaching and Research Farm (OAUTRF), respectively. PM concentration and wind speed showed a negative exponential distribution curve with the lowest exponential fit coefficient of determination (R²) values of 0.08 for PM_2.5 and 0.03 for PM₁₀ during nighttime periods at Eleweran and Fire service sites, respectively. The relationship between PM concentration and temperature gave a decay curve indicating that higher PM concentrations were observed at lower temperatures. The exponential distribution curve for the relationship between PM concentration and relative humidity (RH) showed that PM concentrations do not vary for RH < 80 % while stronger relationship was noticed with higher PM concentration for RH > 80 % for both day and nighttime. The performances of the MLR model were slightly poor and as such not too reliable for predicting the concentration but useful for improving predictive model accuracy when other variables contributing to the variability of PM is considered. The study concluded that the anthropogenic and industrial activities at the smelting factory contribute significantly to the elevated PM mass concentration measured at the study locations.