Mechanism of Spatiotemporal Air Quality Response to Meteorological Parameters: A National-Scale Analysis in China

Elucidating transport dynamics and regional division of PM2.5 and O3 in China using an advanced network model

Air pollution exhibits significant spatial spillover effects, complicating and challenging regional governance models. This study innovatively applied and optimized a statistics-based complex network method in atmospheric environmental field. The methodology was enhanced through improvements in edge weighting and threshold calculations, leading to the development of an advanced pollutant transport network model. This model integrates pollution, meteorological, and geographical data, thereby comprehensively revealing the dynamic characteristics of PM2.5 and O3 transport among various cities in China. Research findings indicated that, throughout the year, the O3 transport network surpassed the PM2.5 network in edge count, average degree, and average weighted degree, showcasing a higher network density, broader city connections, and greater transmission strength. Particularly during the warm period, these characteristics of the O3 network were more pronounced, showcasing significant transport potential. Furthermore, the model successfully identified key influential cities in different periods; it also provided detailed descriptions of the interprovincial spillover flux and pathways of PM2.5 and O3 across various time scales. It pinpointed major pollution spillover and receiving provinces, with primary spillover pathways concentrated in crucial areas such as the Beijing-Tianjin-Hebei (BTH) region and its surrounding areas, the Yangtze River Delta, and the Fen-Wei Plain. Building on this, the model divided the O3, PM2.5, and synergistic pollution transmission regions in China into 6, 7, and 8 zones, respectively, based on network weights and the Girvan Newman (GN) algorithm. Such division offers novel perspectives and strategies for regional joint prevention and control. The validity of the model was further corroborated by source analysis results from the WRF-CAMx model in the BTH area. Overall, this research provides valuable insights for local and regional atmospheric pollution control strategies. Additionally, it offers a robust analytical tool for research in the field of atmospheric pollution.

Effects of urbanisation on PM2.5 concentrations: A systematic review and meta-analysis

While urbanisation greatly improves a population's quality of life, it also has significant effects on urban air pollution. Previous studies have determined how urbanisation affects PM2.5 concentrations; the findings, however, have not been consistent. This study conducts a meta-analysis to systematically organise existing research and draw more conclusive and broadly applicable results regarding the impact of different factors of urbanisation on PM2.5 concentrations. The main research findings are as follows: (1) the Environmental Kuznets Curve (EKC) is proven to hold true in terms of the effect of population and land urbanisation on PM2.5 concentrations, while there is no consistent conclusion on the non-linear relationship between economic urbanisation and PM2.5 concentrations; (2) publication bias is evident in research on the economic and comprehensive urbanisation dimensions under linear assumptions; (3) there are notable heterogeneities in existing research in this field. The meta-regression model further indicates that model design, sample design, and publication characteristics might be responsible for these heterogeneities. This study innovatively applies a meta-analysis to investigate the effect of urbanisation on PM2.5 concentrations. The findings will contribute to scholars designing more rigorous research frameworks in this field.

Heterogeneous Learning of Functional Clustering Regression and Application to Chinese Air Pollution Data

Clustering algorithms are widely used to mine the heterogeneity between meteorological observations. However, traditional applications suffer from information loss due to data processing and pay little attention to the interaction between meteorological indicators. In this paper, we combine the ideas of functional data analysis and clustering regression, and propose a functional clustering regression heterogeneity learning model (FCR-HL), which respects the data generation process of meteorological data while incorporating the interaction between meteorological indicators into the analysis of meteorological data heterogeneity. In addition, we provide an algorithm for FCR-HL to automatically select the number of clusters, which has good statistical properties. In the later empirical study based on PM_2.5 concentrations and PM₁₀ concentrations in China, we found that the interaction between PM₁₀ and PM_2.5 varies significantly between regions, showing several types of significant patterns, which provide meteorologists with new perspectives to further study the effects between meteorological indicators.

Analysis on Spatio-Temporal Evolution and Influencing Factors of Air Quality Index (AQI) in China

After the reform and opening up, China's economy has developed rapidly. However, environmental problems have gradually emerged, the top of which is air pollution. We have used the following methods: In view of the shortcomings of the current spatio-temporal evolution analysis of the Air Quality Index (AQI) that is not detailed to the county level and the lack of analysis of its underlying causes, this study collects the AQI of all counties in China from 2014 to 2021, and uses spatial autocorrelation and other analysis methods to deeply analyze the spatio-temporal evolution characteristic. Based on the provincial panel data, the spatial econometric model is used to explore its influencing factors and spillover effects. The research results show that: (1) From 2014 to 2021, the AQI of all counties in China showed obvious spatial agglomeration characteristics, and counties in central and western Xinjiang, as well as Beijing, Tianjin, and Hebei, were high-value agglomeration areas; (2) the change trend of the AQI value also has obvious spatial autocorrelation, and generally presents a downward trend. However, the AQI value in a small number of regions, such as Xinjiang, shows a slow decline or even a reverse rise; (3) there are some of the main factors affecting AQI, such as GDP per capita, percentage of forest cover, total emissions of SO₂, and these factors have different impacts on different regions. In addition, the increase of GDP per capita, the reduction of industrialization level, and the increase of forest coverage will significantly improve the air quality of other surrounding provinces. An in-depth analysis of the spatio-temporal evolution, influencing factors, and spillover effects of AQI in China is conducive to formulating countermeasures to improve air quality according to local conditions and promoting regional sustainable development.

Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran

Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data.

Malaysia PM10 Air Quality Time Series Clustering Based on Dynamic Time Warping

Air quality monitoring is important in the management of the environment and pollution. In this study, time series of PM10 from air quality monitoring stations in Malaysia were clustered based on similarity in terms of time series patterns. The identified clusters were analyzed to gain meaningful information regarding air quality patterns in Malaysia and to identify characterization for each cluster. PM10 time series data from 5 July 2017 to 31 January 2019, obtained from the Malaysian Department of Environment and Dynamic Time Warping as the dissimilarity measure were used in this study. At the same time, k-Means, Partitioning Around Medoid, agglomerative hierarchical clustering, and Fuzzy k-Means were the algorithms used for clustering. The results portray that the categories and activities of locations of the monitoring stations do not directly influence the pattern of the PM10 values, instead, the clusters formed are mainly influenced by the region and geographical area of the locations.

Google Earth Engine based spatio-temporal analysis of air pollutants before and during the first wave COVID-19 outbreak over Turkey via remote sensing

Air pollution is one of the vital problems for the sustainability of cities and public health. The lockdown caused by the COVID-19 outbreak has become a natural laboratory, enabling to investigate the impact of human/industrial activities on the air pollution. In this study, we investigated the spatio-temporal density of TROPOMI-based nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) products, and MODIS-derived Aerosol Optical Depth (AOD) from January 2019 to September 2020 (also covering the first wave of the COVID-19) over Turkey using Google Earth Engine (GEE). The results showed a significant decrease in NO₂ and AOD, while SO₂ unchanged and had slightly higher concentrations in some regions during the lockdown compared to 2019. The relationship between air pollutants and meteorological parameters during the lockdown showed that air temperature and pressure were highly correlated with air pollutants, unlike precipitation and wind speed. Moreover, Purchasing Managers' Index (PMI) data, indicator of economic/industrial activities, also provided poor correlation with air pollutants. TROPOMI-based NO₂ and SO₂ were compared with station-based pollutants for three sites (suburban, urban, and urban-traffic classes) in Istanbul, revealing 0.83, 0.70 and 0.65 correlation coefficients for NO₂, respectively, while SO₂ showed no significant correlation. Besides, AOD data were validated using two AERONET sites providing 0.86 and 0.82 correlation coefficients. Overall, the satellite-based data provided significant outcomes for the spatio-temporal evaluation of air quality, especially during the first wave of the COVID-19 lockdown.

Measurement of Indoor Air Pollution in Bhutanese Households during Winter: An Implication of Different Fuel Uses

Measurements of indoor air pollution in Bhutanese households were conducted in winter with regards to the use of different fuels. These measurements were taken in Thimphu, Bhutan, for PM1, PM2.5, PM10, CO, temperature, air pressure and relative humidity in houses and offices with various fuels used for heaters and classified as the hospital, NEC, kerosene, LPG and firewood. The objective of this study was to measure the pollutant concentrations from different fuel uses and to understand their relationship to the different fuel uses and meteorological data using a time series and statistical analysis. The results revealed that the average values for each pollutant for the categories of the hospital, NEC, kerosene, LPG and firewood were as follows: CO (ppm) were 6.50 ± 5.16, 3.65 ± 1.42, 31.04 ± 18.17, 33.93 ± 26.41, 13.92 ± 17.58, respectively; PM2.5 (μg·m−3) were 7.24 ± 4.25, 4.72 ± 0.71, 6.01 ± 3.28, 5.39 ± 2.62, 18.31 ± 11.92, respectively; PM10 (μg·m−3) was 25.44 ± 16.06, 10.61 ± 4.39, 11.68 ± 6.36, 22.13 ± 9.95, 28.66 ± 16.35, respectively. Very coarse particles of PM10 were identified by outdoor infiltration for the hospital, NEC, kerosene and LPG that could be explained by the stable atmospheric conditions enhancing accumulation of ambient air pollutions during the measurements. In addition, high concentrations of CO from kerosene, LPG and firewood were found to be mainly from indoor fuel combustion. Firewood was found to the most polluting fuel for particulate matter concentrations. For the relationships of PM and meteorological data (Temp, RH and air pressure), they were well explained by linear regression while those for CO and the meteorological data, they were well explained by polynomial regression. Since around 40% of houses in Thimphu, Bhutan, use firewood for heating, it is recommended that ventilation should be improved by opening doors and windows in houses with firewood heaters to help prevent exposure to high concentrations of PM1, PM2.5, and PM10.

The effect of air-pollution and weather exposure on mortality and hospital admission and implications for further research: A systematic scoping review

BACKGROUND

Air-pollution and weather exposure beyond certain thresholds have serious effects on public health. Yet, there is lack of information on wider aspects including the role of some effect modifiers and the interaction between air-pollution and weather. This article aims at a comprehensive review and narrative summary of literature on the association of air-pollution and weather with mortality and hospital admissions; and to highlight literature gaps that require further research.

METHODS

We conducted a scoping literature review. The search on two databases (PubMed and Web-of-Science) from 2012 to 2020 using three conceptual categories of "environmental factors", "health outcomes", and "Geographical region" revealed a total of 951 records. The narrative synthesis included all original studies with time-series, cohort, or case cross-over design; with ambient air-pollution and/or weather exposure; and mortality and/or hospital admission outcomes.

RESULTS

The final review included 112 articles from which 70 involved mortality, 30 hospital admission, and 12 studies included both outcomes. Air-pollution was shown to act consistently as risk factor for all-causes, cardiovascular, respiratory, cerebrovascular and cancer mortality and hospital admissions. Hot and cold temperature was a risk factor for wide range of cardiovascular, respiratory, and psychiatric illness; yet, in few studies, the increase in temperature reduced the risk of hospital admissions for pulmonary embolism, angina pectoris, chest, and ischemic heart diseases. The role of effect modification in the included studies was investigated in terms of gender, age, and season but not in terms of ethnicity.

CONCLUSION

Air-pollution and weather exposure beyond certain thresholds affect human health negatively. Effect modification of important socio-demographics such as ethnicity and the interaction between air-pollution and weather is often missed in the literature. Our findings highlight the need of further research in the area of health behaviour and mortality in relation to air-pollution and weather, to guide effective environmental health precautionary measures planning.

Cluster Analysis of Haze Episodes Based on Topological Features

Severe haze episodes have periodically occurred in Southeast Asia, specifically taunting Malaysia with adverse effects. A technique called cluster analysis was used to analyze these occurrences. Traditional cluster analysis, in particular, hierarchical agglomerative cluster analysis (HACA), was applied directly to data sets. The data sets may contain hidden patterns that can be explored. In this paper, this underlying information was captured via persistent homology, a topological data analysis (TDA) tool, which extracts topological features including components, holes, and cavities in the data sets. In particular, an improved version of HACA was proposed by combining HACA and persistent homology. Additionally, a comparative study between traditional HACA and improved HACA was done using particulate matter data, which was the major pollutant found during haze episodes by the Klang, Petaling Jaya, and Shah Alam air quality monitoring stations. The effectiveness of these two clustering approaches was evaluated based on their ability to cluster the months according to the haze condition. The results showed that clustering based on topological features via the improved HACA approach was able to correctly group the months with severe haze compared to clustering them without such features, and these results were consistent for all three locations.

Spatial Association Pattern of Air Pollution and Influencing Factors in the Beijing-Tianjin-Hebei Air Pollution Transmission Channel: A Case Study in Henan Province

The Beijing–Tianjin–Hebei (BTH) air pollution transmission channel and its surrounding areas are of importance to air pollution control in China. Based on daily data of air quality index (AQI) and air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃) from 2015 to 2016, this study analyzed the spatial and temporal characteristics of air pollution and influencing factors in Henan Province, a key region of the BTH air pollution transmission channel. The result showed that non-attainment days and NAQI were slightly improved at the provincial scale during the study period, whereas that in Hebi, Puyang, and Anyang became worse. PM_2.5 was the largest contributor to the air pollution in all cities based on the number of non-attainment days, but its mean frequency decreased by 21.62%, with the mean occurrence of O₃ doubled. The spatial distribution of NAQI presented a spatial agglomeration pattern, with high-high agglomeration area varying from Jiaozuo, Xinxiang, and Zhengzhou to Anyang and Hebi. In addition, the NAQI was negatively correlated with sunshine duration, temperature, relative humidity, wind speed, and positively to atmospheric pressure and relative humidity in all four clusters, whereas relationships between socioeconomic factors and NAQI differed among them. These findings highlight the need to establish and adjust regional joint prevention and control of air pollution as well as suggest that it is crucially important for implementing effective strategies for O₃ pollution control.

Analysis of Spatio-temporal Characteristics and Driving Forces of Air Quality in the Northern Coastal Comprehensive Economic Zone, China

Comprehensive analysis of air quality is essential to underpin knowledge-based air quality conservation policies and funding decisions by governments and managers. In this paper, air quality change characteristics for the Northern Coastal Comprehensive Economic Zone from 2008 to 2018 were analyzed using air quality indices. The spatio-temporal pattern of air quality was identified using centroid migration, spatial autocorrelation analysis and spatial analysis in a geographic information system (GIS). A spatial econometric model was established to confirm the natural and anthropogenic factors affecting air quality. Results showed that air pollution decreased significantly. PM2.5, PM10, and O3 were the primary pollutants. The air quality exhibited an inverted U-shaped trend from January to December, with the highest quality being observed in summer and the lowest during winter. Spatially, the air quality showed an increasing trend from inland to the coast and from north to south, with significant spatial autocorrelation and clustering. Population, energy consumption, temperature, and atmospheric pressure had significant negative impacts on air quality, while wind speed had a positive impact. This study offers an efficient and effective method to evaluate air quality change. The research provides important scientific information necessary for developing future air pollution prevention and control.

Inter-city air pollutant transport in The Beijing-Tianjin-Hebei urban agglomeration: Comparison between the winters of 2012 and 2016

In recent years, with the continual urbanization of China, regional atmospheric environmental problems have become increasingly prominent. Although local emissions are an important cause of local pollution, the cross-boundary transmission of pollutants between cities also has an inevitable impact on regional pollution. In this study, the Weather Research and Forecasting (WRF) model coupled with the California Puff (CALPUFF) air quality model was used to study the transmission characteristics of four major air pollutants (SO₂, NO_x, PM_2.5 and PM₁₀) in the Beijing-Tianjin-Hebei urban agglomeration in China in winter, which is the season characterized by the highest levels of pollution. This urban agglomeration is the most polluted area in China. We compared the emission and transmission of pollutants at the city level based on data for January 2012 and 2016. We found that the emissions of most cities had declined since the implementation, in 2013, of the "Action Plan for the Prevention and Control of Air Pollution". However, the emissions of three cities, Zhangjiakou, Chengde and Baoding, had significantly increased. Furthermore, the "receptor cities" and "source cities" also showed some changes. For example, in 2016, Chengde and Zhangjiakou changed from receptor to source cities, while Tangshan and Tianjin showed the opposite change in status. The likely reason for these changes was that some industries in heavily polluted cities had moved due to stringent atmospheric pollution policies. Moreover, the transmission range of source cities (e.g., Shijiazhuang) in 2016 was significantly smaller than that in 2012, and the transmission intensity also decreased. This case study aids our understanding of how inter-city air pollution transmission has been affected by environmental policy.