Spatiotemporal characteristics of PM2.5 and PM10 at urban and corresponding background sites in 23 cities in China

Unveiling the potential of a novel portable air quality platform for assessment of fine and coarse particulate matter: in-field testing, calibration, and machine learning insights

Although low-cost air quality sensors facilitate the implementation of denser air quality monitoring networks, enabling a more realistic assessment of individual exposure to airborne pollutants, their sensitivity to multifaceted field conditions is often overlooked in laboratory testing. This gap was addressed by introducing an in-field calibration and validation of three PAQMON 1.0 mobile sensing low-cost platforms developed at the Mining and Metallurgy Institute in Bor, Republic of Serbia. A configuration tailored for monitoring PM2.5 and PM10 mass concentrations along with meteorological parameters was employed for outdoor measurement campaigns in Bor, spanning heating (HS) and non-heating (NHS) seasons. A statistically significant positive linear correlation between raw PM2.5 and PM10 measurements during both campaigns (R > 0.90, p ≤ 0.001) was observed. Measurements obtained from the uncalibrated NOVA SDS011 sensors integrated into the PAQMON 1.0 platforms exhibited a substantial and statistically significant correlation with the GRIMM EDM180 monitor (R > 0.60, p ≤ 0.001). The calibration models based on linear and Random Forest (RF) regression were compared. RF models provided more accurate descriptions of air quality, with average adjR2 values for air quality variables in the range of 0.70 to 0.80 and average NRMSE values between 0.35 and 0.77. RF-calibrated PAQMON 1.0 platforms displayed divergent levels of accuracy across different pollutant concentration ranges, achieving a data quality objective of 50% during both measurement campaigns. For PM2.5, uncertainty ( U r ) was below 50% for concentrations between 9.06 and 34.99 μg/m3 in HS and 5.75 and 17.58 μg/m3 in NHS, while for PM10, it stayed below 50% from 19.11 to 51.13 μg/m3 in HS and 11.72 to 38.86 μg/m3 in NHS.

Health Impacts of Fine Particulate Matter Shift Due to Urbanization in China

Rapid urbanization and industrialization have resulted in diverse anthropogenic activities and emissions between urban and non-urban regions, leading to varying levels of exposure to air pollutants and associated health risks. However, endeavors to mitigate air pollution and health benefits have displayed considerable heterogeneity across different regions. Therefore, comprehending the changes in air pollutant concentrations and health impacts within an urbanization context is imperative for promoting environmental equity. This paper uses gross domestic product (GDP)- and population-weighted methods to distinguish anthropogenic emissions from urban and non-urban areas in China and quantified their contributions to fine particulate matter (PM2.5) using the Community Multiscale Air Quality (CMAQ) model in 2010 and 2019. Anthropogenic emissions from urban and non-urban (outside urban) regions decreased by 26 and 44% from 2010 to 2019, respectively, resulting in 31 and 28% reductions of PM2.5 in China. PM2.5-related premature mortality attributed to non-urban and urban anthropogenic emission decreases by 8%. Non-urban anthropogenic activities are the main contributor to PM2.5 (56% in 2010 and 2019) and its associated premature mortality (59%), which also predominantly affects non-urban premature mortality (37-42% in 2010-2019). Population changes increase the proportion of premature mortality in urban populations (7-19%) from 2010 to 2019. This study emphasizes the shift of affected populations due to urbanization and population changes.

Selective capture of PM2.5 by urban trees: The role of leaf wax composition and physiological traits in air quality enhancement

Human health risks from particles with a diameter of less than 2.5 µm (PM2.5) highlight the role of urban trees as bio-filters in air pollution control. However, whether the size and composition of particles captured by various tree species differ or not remain unclear. This study investigates how leaf attributes affect the capture of PM2.5, which can penetrate deep into the lungs and pose significant health risks. Using a self-developed particulate matter (PM) resuspension chamber and single-particle aerosol mass spectrometer, we measured the size distribution and mass spectra of particles captured by ten tree species. Notably, Cinnamomum camphora (L.) J.Presl and Osmanthus fragrans Lour. are more effective at capturing particles under 1 µm, which are most harmful because they can reach the alveoli, whereas Ginkgo biloba L. and Platanus × acerifolia (Aiton) Willd. tend to capture larger particles, up to 1.6 µm, which are prone to being trapped in the upper respiratory tract. Leaf physiological traits such as stomatal conductance and water potential significantly enhance the capture of larger particles. The Adaptive Resonance Theory neural network (ART-2a) algorithm classified a large number of single particles to determine their composition. Results indicate distinct inter-species variations in chemical composition of particles captured by leaves. Moreover, we identified how specific leaf wax compositions-beyond the known sticky nature of hydrophobic waxes-contribute to particle adhesion, particularly highlighting the roles of fatty acids and alkanes in adhering particles rich in organic carbon and heavy metals, respectively. This research advances our understanding by linking leaf physiological and wax characteristics to the selective capture of PM2.5, providing actionable insights for urban forestry management. The detailed exploration of particle size and composition, tied to specific tree species, enriches the current literature by quantifying how and why different species contribute variably to air quality improvement. This adds a crucial layer of specificity to the general knowledge that trees serve as bio-filters, offering a refined strategy for planting urban trees based on their particulate capture profiles.

Changes of PM2.5 and O3 and their impact on human health in the Guangdong-Hong Kong-Macao Greater Bay Area

In recent years, the combined pollution of PM2.5 and O3 in China, particularly in economically developed regions such as the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), has garnered significant attention due to its potential implications. This study systematically investigated the changes of PM2.5 and O3 and their associated human health effects in the GBA, utilizing observational data spanning from 2015 to 2019. The findings revealed a spatial trend indicating a gradual decrease in PM2.5 levels from the northwest to the southeast, while the spatial distribution of MDA8 O3 demonstrated an opposing pattern to that of PM2.5. The monthly fluctuations of PM2.5 and MDA8 O3 exhibited V-shaped and M-shaped patterns, respectively. Higher MDA8 O3 concentrations were observed in autumn, followed by summer and spring. Over the five-year period, PM2.5 concentrations exhibited a general decline, with an annual reduction rate of 1.7 μg m-3/year, while MDA8 O3 concentrations displayed an annual increase of 3.2 μg m-3. Among the GBA regions, Macao, Foshan, Guangzhou, and Jiangmen demonstrated notable decreases in PM2.5, whereas Jiangmen, Zhongshan, and Guangzhou experienced substantial increases in MDA8 O3 levels. Long-term exposure to PM2.5 in 2019 was associated with 21,113 (95% CI 4968-31,048) all-cause deaths (AD), 1333 (95% CI 762-1714) cardiovascular deaths (CD), and 1424 (95% CI 0-2848) respiratory deaths (RD), respectively, reflecting declines of 27.6%, 28.0%, and 28.4%, respectively, compared to 2015. Conversely, in 2019, estimated AD, CD, and RD attributable to O3 were 16,286 (95% CI 8143-32,572), 7321 (95% CI 2440-14,155), and 6314 (95% CI 0-13,576), respectively, representing increases of 45.9%, 46.2%, and 44.2% over 2015, respectively. Taken together, these findings underscored a shifting focus in air pollution control in the GBA, emphasizing the imperative for coordinated control strategies targeting both PM2.5 and O3.

An exploratory analysis of PM 2.5 /PM 10 ratio during spring 2016-2018 in Metropolitan Lima

Aerosols (PM2.5 and PM10 ) represent one of the most critical pollutants due to their negative effects on human health. This research analyzed the relationship of PM and its PM2.5 /PM10 ratios with climatic variables in the austral spring (2016-2018) in Metropolitan Lima. Overall, there was an average PM2.5 /PM10 ratio of 0.33 with fluctuations from 0.30 to 0.35. However, there have also been high point values that reached ratios greater than one. This situation indicates a moderate condition of contamination by particulate matter with a predominance of coarse aerosols in spring, with an increasing trend over the years. The locations Ate and Villa Maria del Triunfo, especially Ate, presented poor quality conditions. Thursdays showed outstanding pollution peaks by PM10 , and a decrease is visible on Sundays. On the other hand, the PM2.5 showed a similar pattern every day, including Sundays. The maximum peaks occurred in the morning and night hours. The increase in anthropogenic emissions associated with the formation of secondary aerosols has been evident, being the case of the location Campo de Marte, the one that had a significant increase in ratios PM2.5 /PM10 , which confirms a greater intensity of secondary formations of carbonaceous particles from industrial oil sources, vehicle exhaust, as well as aerosols from metal smelting and biomass burning. There were negative correlations of the ratios with PM10 , temperature, wind speed, and direction, and positive correlations with PM2.5 and relative humidity. Contour lines were successfully developed that demonstrated the interaction of climate with PM2.5 /PM10 ratios. This will deepen the exploration of emission sources and modeling, which allows for optimizing air quality indices to control emissions and adequately manage air quality in Metropolitan Lima.

Spatiotemporal distribution, light absorption characteristics, and source apportionments of black and brown carbon in China

Black carbon (BC) and brown carbon (BrC) are aerosols that absorb light and thereby contribute to climate change. In this study, the light absorption properties and spatiotemporal distributions of equivalent BC (eBC) and BrC aerosols were determined based on continuous measurements of aerosol light absorption from January to August 2017, using a seven-channel aethalometer at 49 sampling sites in China. The source apportionments of BC and BrC were identified using the BC/PM2.5, absorption Ångström exponent, the concentration-weighted trajectory method, and the random forest model. Based on the results, BC was the dominant light absorber, whereas BrC was responsible for a higher proportion of the light absorption in northern compared to southern China. The light absorption of BrC was highest in winter (34.3 Mm-1), followed by spring (19.0 Mm-1) and summer (3.6 Mm-1). The combustion of liquid fuels accounted for over 50 % of the light absorption coefficient of BC in most cities and the importance of carbon monoxide (CO) and nitrogen dioxide (NO2) was over 10 % for BC emitted by liquid fuel combustion, based on the random forest model. The contribution of solid fuel combustion to BC in the north was larger than that in the southern regions as coal combustion and crop residue burning are important emission sources of BC in most northern cities. The contribution of primary BrC to light absorption was high in some northern cities, whereas that of secondary BrC was prevalent in some southern cities. The diurnal variations in secondary BrC were affected by changes in odd oxygen and relative humidity, which promoted the photobleaching of the chromophores and aqueous-phase reactions of secondary BrC.

Trace elements and microbial community composition associated with airborne PM2.5 in wetlands: A case study in Tablas de Daimiel National Park

Tablas de Daimiel National Park (TDNP) is one of the most important wetlands in the Iberian Peninsula. Due to its location near various cities and new industries focused on agricultural waste revalorization, we investigated concurrently the concentrations of particulate matter 2.5 (PM2.5) mass, trace element composition, and associated microbial communities (bacteria and fungi) during a year-long study. The goal of this study was to explore the dependencies among these physicochemical and microbiological parameters on a seasonal time scale. Additionally, we assessed meteorological conditions and back trajectories to shed light on atmospheric mechanisms and sources related to these elements. We found the variability of PM2.5 to be influenced by local meteorological parameters. Through the analysis of crustal enrichment factors (EFs), bivariate correlations, and air mass patterns, we determined that soil resuspension was the primary contributor to elevated metal concentrations in PM2.5 within the park, followed by other minor sources, such as traffic emissions and Sahara dust intrusions. The measured metal levels were used to calculate the ecological risk in the area, resulting in a low ecological risk index (RI) of 52. Shifts in microbial community structure were observed to be mainly driven by changes in air temperature and Cu concentration. The results from this study contribute to a better understanding of the environmental dynamics in TDNP. Taken together, our findings will aid in the development of effective strategies for its conservation and management.

Evolution in disparity of PM2.5 pollution in China

The spatial disparity of air pollutants is one of the key influential factors for environmental inequality. We quantitatively evaluated the evolution of PM2.5 spatial disparity in China during 2013-2020, and investigated the associations between PM2.5 spatial disparity and economic indicators. Differences in PM2.5 between more- and less-polluted cities declined over time, suggesting decreased absolute disparity. However, the more polluted cities in 2013 remained so in 2017 and 2020, and vice versa, indicating persistent relative disparity. PM2.5 pollution levels increased with higher GDP per capita in less-developed areas of China, but such negative effects weakened over time, while economic development tended to promote cleaner air in developed areas of China. Therefore, policies to improve air quality and promote economic development simultaneously are needed in China to reduce the disparity of air pollution and promote all people to enjoy environmental equality.

Assessment of air quality before and during the COVID-19 and its potential health impacts in an arid oasis city: Urumqi, China

As a key node city of the "Silk Road Economic Belt" Urumqi has been listed as one of the ten most polluted cities in the world, posing a serious threat to the urban environment and residents' health. This study analyzed the air quality before and during the COVID-19 (Coronavirus disease 2019) pandemic and its potential health effects based on the data of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃_8h levels from 10 air quality monitoring stations in Urumqi from January 1, 2017, to December 31, 2021. As per the results, the concentrations of the air pollutants PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃_8h in Urumqi from 2017 to 2021 showed a cyclical trend, and the implementation of COVID-19 prevention and control measures could effectively reduce the concentration(ρ) of air pollutants. The mean value of ρ(PM_2.5) decreased from 2017 to 2021, whereas ρ(O₃_8h) showed a waveform change trend (increased in 2017-2018, decreased in 2018-2020, and increased after 2020). Meanwhile, the maximum annual average values of ρ(PM_2.5) and ρ(O₃_8h) for the six monitoring stations during 2017-2021 occurred at sites S2 (74.37 µg m^-3) and S6 (91.80 µg m^-3), respectively; rapid industrialization had a greater impact on PM_2.5 and O₃_8h concentrations compared to commercial and residential areas. In addition, the air quality index data series can characterize the fluctuation trend of PM_2.5. The high pollution levels (Class IV and V) of the air pollutants PM_2.5 and O₃_8h in Urumqi have been decreasing annually, and good days can account for 80-95% of the total number of days in the year, indicating that the number of days with a potential threat to residents' health is gradually decreasing. Therefore, more attention should be paid in controlling and managing air pollution in Urumqi.

Spatiotemporal Regularity and Socioeconomic Drivers of the AQI in the Yangtze River Delta of China

Air pollution has caused adverse effects on the climate, the ecological environment and human health, and it has become a major challenge facing the world today. The Yangtze River Delta (YRD) is the region with the most developed economy and the most concentrated population in China. Identifying and quantifying the spatiotemporal characteristics and impact mechanism of air quality in this region would help in formulating effective mitigation policies. Using annual data on the air quality index (AQI) of 39 cities in the YRD from 2015 to 2018, the spatiotemporal regularity of the AQI is meticulously uncovered. Furthermore, a geographically weighted regression (GWR) model is used to qualify the geographical heterogeneity of the effect of different socioeconomic variables on the AQI level. The empirical results show that (1) the urban agglomeration in the YRD presents an air pollution pattern of being low in the northwest and high in the southeast. The spatial correlation of the distribution of the AQI level is verified. The spatiotemporal regularity of the “high clustering club” and the “low clustering club” is obvious. (2) Different socioeconomic factors show obvious geographically heterogeneous effects on the AQI level. Among them, the impact intensity of transportation infrastructure is the largest, and the impact intensity of the openness level is the smallest. (3) The upgrading of the industrial structure improves the air quality status in the northwest more than it does in the southeast. The impact of transportation infrastructure on the air pollution of cities in Zhejiang Province is significantly higher than the impact on the air pollution of other cities. The air quality improvement brought by technological innovation decreases from north to south. With the expansion of urban size, there is a law according to which air quality first deteriorates and then improves. Finally, the government should promote the upgrading of key industries, reasonably control the scale of new construction land, and increase the cultivation of local green innovative enterprises.

Spatiotemporal Variation in Air Pollution Characteristics and Influencing Factors in Ulaanbaatar from 2016 to 2019

Ambient air pollution is a global environmental issue that affects human health. Ulaanbaatar (UB), the capital of Mongolia, is one of the most polluted cities in the world, and it is of great importance to study the temporal and spatial changes in air pollution in this city, along with their influencing factors. To understand the characteristics of atmospheric pollutants in UB, the contents of PM10, PM2.5, SO2, NO2, CO, and O3, as well as their influencing factors, were analyzed from data obtained from automatic air quality monitoring stations. These analyses yielded six major findings: (1) From 2016 to 2019, there was a total of 883 pollution days, and PM2.5 and PM10 were the primary pollutants on 553 and 351 of these days, respectively. The air pollution was dominated by PM10 in spring and summer, affected by both PM2.5 and PM10 in autumn, and dominated by PM2.5 in winter. (2) Compared with 2016, the number of days with good air quality in UB in 2019 increased by 45%, and the number of days with unhealthy or worse levels of pollution decreased by 56%, indicating that the air quality improved year by year. (3) From 2016 to 2019, the annual average PM2.5/PM10 ratio dropped from 0.55 to 0.45, and the proportion of PM2.5 in particulate matter decreased year by year. The PM concentration and PM2.5/PM10 ratio were highest in winter and lowest in summer. When comparing the four-season averages, the average PM2.5 concentration decreased by 89% from its highest level, and the PM10 concentration decreased by 67%, indicating stronger seasonal differences in PM2.5 than in PM10. (4) The hourly changes in PM concentration showed a bimodal pattern, exhibiting a decrease during the day and a slight increase in the afternoon due to temperature inversion, so the PM2.5/PM10 ratio increased at night in all four seasons. The PM concentration during the heating season was significantly higher than that in the non-heating season, indicating that coal-fired heating was the main cause of air pollution in UB. (5) Sand dust and soot were the two main types of pollution in UB. (6) Correlation analysis and linear fitting analysis showed that PM2.5 and PM10 caused by coal-firing had an important impact on air quality in UB. Coal combustion and vehicle emissions with SO2, NO2, and CO as factors made large contributions to PM2.5.

Real-Time Synchronous 3-D Detection of Air Pollution and Wind Using a Solo Coherent Doppler Wind Lidar

The monitoring and tracking of urban air pollution is a challenging environmental issue. The approach of synchronous 3-D detection of wind and pollution using a solo coherent Doppler wind lidar (CDWL) is developed and demonstrated. The 3-D distribution of pollutant is depicted by the backscatter coefficient based on signal intensity of CDWL. Then, a high-resolution wind field is derived to track the local air pollution source with its diffusion and to analyze transboundary air pollution episodes. The approach is experimentally implemented in a chemical industry park. Smoke plumes caused by point source pollutions are captured well using plan position indicator (PPI) scanning with low elevation. A typical source of pollution is located, combining the trajectory of the smoke plume and the horizontal wind vector. In addition, transboundary air pollution caused by the transport of dust storms is detected in a vertical profile scanning pattern, which is consistent with the results of national monitoring stations and backward trajectory models. Our present work provides a significant 3-D detection approach to air pollution monitoring with its sources, paths, and heights by using a solo-CDWL system.

Spatiotemporal variations of ambient air pollutants and meteorological influences over typical urban agglomerations in China during the COVID-19 lockdown

To investigate the air quality change during the COVID-19 pandemic, we analyzed spatiotemporal variations of six criteria pollutants in nine typical urban agglomerations in China using ground-based data and examined meteorological influences through correlation analysis and backward trajectory analysis under different responses. Concentrations of PM_2.5, PM₁₀, NO₂, SO₂ and CO in urban agglomerations respectively decreased by 18%-45% (30%-62%), 17%-53% (22%-39%), 47%-64% (14%-41%), 9%-34% (0%-53%) and 16%-52% (23%-56%) during Lockdown (Post-lockdown) period relative to Pre-lockdown period. PM_2.5 pollution events occurred during Lockdown in Beijing-Tianjin-Hebe (BTH) and Middle and South Liaoning (MSL), and daily O₃ concentration rose to grade Ⅱ standard in Post-lockdown period. Distinct from the nationwide slump of NO₂ during Lockdown period, a rebound (∼40%) in Post-lockdown period was observed in Cheng-Yu (CY), Yangtze River Middle-Reach (YRMR), Yangtze River Delta (YRD) and Pearl River Delta (PRD). With slightly higher wind speed compared with 2019, the reduction of PM_2.5 (51%-62%) in Post-lockdown period is more than 2019 (15%-46%) in HC (Harbin-Changchun), MSL, BTH, CP (Central Plain) and SP (Shandong-Peninsula), suggesting lockdown measures are effective to PM_2.5 alleviation. Although O₃ concentrations generally increased during the lockdown, its increment rate declined compared with 2019 under similar sunlight duration and temperature. Additionally, unlike HC, MSL and BTH, which suffered from additional (> 30%) air masses from surrounding areas after the lockdown, the polluted air masses reaching YRD and PRD mostly originated from the long-distance transport, highlighting the importance of joint regional governance.

Spatial characteristics of the PM2.5/PM10 ratio and its indicative significance regarding air pollution in Hebei Province, China

Particulate matter (PM) is the primary air pollutant in northern China. The PM_2.5/PM₁₀ ratio has been used increasingly as an indicator to reflect anthropogenic PM pollution, but its advantages compared with individual PM_2.5 or PM₁₀ concentrations have not been proven sufficiently by experimental data. By dividing Hebei Province (China) into seven natural ecological regions, this study investigated the spatial characteristics of the PM_2.5/PM₁₀ ratio and its relationships with PM_2.5, PM₁₀, economic density, and wind speed. Results showed that the PM_2.5/PM₁₀ ratio decreased from east to west and from south to north, with an annual average value in 2019 of 0.439-0.559. The characteristics of the spatial variation of the PM_2.5/PM₁₀ ratio were different to those of either PM_2.5 or PM₁₀ concentration, indicating that PM pollution reflected by the PM_2.5/PM₁₀ ratio is not entirely consistent with that by PM_2.5 and PM₁₀ concentrations. In comparison with PM_2.5 or PM₁₀ concentration, the PM_2.5/PM₁₀ ratio had higher (lower) correlation with economic density (wind speed), indicating that the PM_2.5/PM₁₀ ratio is a better indicator used to reflect the intensity of anthropogenic emissions of PM pollutants. According to the characteristics of the spatial variations of the PM_2.5/PM₁₀ ratio and the PM_2.5 and PM₁₀ concentrations, the seven ecological regions of Hebei Province were categorized into four different types of atmospheric PM pollution: "three low regions," "three high regions," "one high and two low regions," and "one low and two high regions." This reflects the comprehensive effect of the intensity of anthropogenic PM emissions and the atmospheric diffusion conditions.

Ambient PM2.5 exposure and hospital cost and length of hospital stay for respiratory diseases in 11 cities in Shanxi Province, China

BACKGROUND

Few studies have examined the effects of ambient particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5) on hospital cost and length of hospital stay for respiratory diseases in China.

METHODS

We estimated ambient air pollution exposure for respiratory cases through inverse distance-weighted averages of air monitoring stations based on their residential address and averaged at the city level. We used generalised additive models to quantify city-specific associations in 11 cities in Shanxi and a meta-analysis to estimate the overall effects. We further estimated respiratory burden attributable to PM2.5 using the standards of WHO (25 µg/m3) and China (75 µg/m3) as reference.

RESULTS

Each 10 µg/m3 increase in lag03 PM2.5 corresponded to 0.53% (95% CI: 0.33% to 0.73%) increase in respiratory hospitalisation, an increment of 3.75 thousand RMB (95% CI: 1.84 to 5.670) in hospital cost and 4.13 days (95% CI: 2.51 to 5.75) in length of hospital stay. About 9.7 thousand respiratory hospitalisations, 132 million RMB in hospital cost and 145 thousand days of hospital stay could be attributable to PM2.5 exposures using WHO's guideline as reference. We estimated that 193 RMB (95% CI: 95 to 292) in hospital cost and 0.21 days (95% CI: 0.13 to 0.30) in hospital stay could be potentially avoidable for an average respiratory case.

CONCLUSION

Significant respiratory burden could be attributable to PM2.5 exposures in Shanxi Province, China. The results need to be factored into impact assessment of air pollution policies to provide a more complete indication of the burden addressed by the policies.

Coordinated control of PM2.5 and O3 is urgently needed in China after implementation of the "Air pollution prevention and control action plan"

To improve air quality, China formulated the Air Pollution Prevention and Control Action Plan (APPCAP) in 2013. In the present study, the changes in the concentration of air pollutants after the implementation of APPCAP were investigated based on nationwide monitoring data. From the results, it is evident that the annual mean concentrations of PM_2.5, PM₁₀, SO₂, and CO show a significant downward trend over 2015-2018, with decreasing rates of 3.4, 4.1, 3.8, and 70 μg m^-3/year, respectively. However, no significant change was found in NO₂ while maximum daily 8 h average O₃ concentration (MDA8 O₃) was increased by 3.4 μg m^-3/year during the four years. Spatially, the highest decrease in PM_2.5 was found in Beijing-Tianjin-Hebei (BTH), followed by central China and northeast China, while the Pearl River Delta (PRD), Yungui Plateau, and northwest China showed less decreases. MDA8 O₃ had a higher increase in BTH, central China, Yangtze River Delta (YRD), and PRD. With the decrease in PM_2.5 in recent years, cumulative population exposure to PM_2.5 gradually decreased, whereas there was still more than 65% of the population exposing to annual PM_2.5 higher than the standard of 35 μg m^-3 in 2018. In contrast, the health effects of O₃ gradually increased with 13.1%, 14.3%, 20.4%, and 21.7% of the population exposed to unhealthy O₃ levels in summer from 2015 to 2018. O₃ pollution is causing severe health risks with estimated nationwide mortality of 70,024 (95% CI: 55,510-84,501), 79,159 (95% CI: 62,750-95,525), 105,150 (95% CI: 83,378-126,852), and 104,404 (95% CI: 82,784-125,956) in the four years, respectively. This clearly shows that the target of air pollution control in China shifts and coordinated control of PM_2.5 and O₃ is urgently needed after the successful implementation of APPCAP.

Regional scenario of air pollution in lockdown due to COVID-19 pandemic: Evidence from major urban agglomerations of India

Air pollution in India during COVID-19 lockdown, which imposed on 25th March to 31st May 2020, has brought a significant improvement in air quality. The present paper mainly focuses on the scenario of air pollution level (PM_2.5, PM₁₀, SO₂, NO₂ and O₃) across 57 urban agglomerations (UAs) of India during lockdown. For analysis, India has been divided into six regions - Northern, Western, Central, Southern, Eastern and North-Eastern. Various spatial statistical modelling with composite air quality index (CAQI) have been utilised to examine the spatial pattern of air pollution level. The result shows that concentration of all air pollutants decreased significantly (except O₃) during lockdown. The maximum decrease is the concentration of NO₂ (40%) followed by PM_2.5 (32%), PM₁₀ (24%) and SO₂ (18%). Among 57 UA's, only five - Panipat (1.00), Ghaziabad (0.76), Delhi (0.74), Gurugram (0.72) and Varanasi (0.71) had least improvement in air pollution level considering entire lockdown period. The outcome of this study has an immense scope to understand the regional scenario of air pollution level and to implement effective strategies for environmental sustainability.

Variation Characteristics and Transportation of Aerosol, NO2, SO2, and HCHO in Coastal Cities of Eastern China: Dalian, Qingdao, and Shanghai

This paper studied the method for converting the aerosol extinction to the mass concentration of particulate matter (PM) and obtained the spatio-temporal distribution and transportation of aerosol, nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO) based on multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations in Dalian (38.85°N, 121.36°E), Qingdao (36.35°N, 120.69°E), and Shanghai (31.60°N, 121.80°E) from 2019 to 2020. The PM2.5 measured by the in situ instrument and the PM2.5 simulated by the conversion formula showed a good correlation. The correlation coefficients R were 0.93 (Dalian), 0.90 (Qingdao), and 0.88 (Shanghai). A regular seasonality of the three trace gases is found, but not for aerosols. Considerable amplitudes in the weekly cycles were determined for NO2 and aerosols, but not for SO2 and HCHO. The aerosol profiles were nearly Gaussian, and the shapes of the trace gas profiles were nearly exponential, except for SO2 in Shanghai and HCHO in Qingdao. PM2.5 presented the largest transport flux, followed by NO2 and SO2. The main transport flux was the output flux from inland to sea in spring and winter. The MAX-DOAS and the Copernicus Atmosphere Monitoring Service (CAMS) models’ results were compared. The overestimation of NO2 and SO2 by CAMS is due to its overestimation of near-surface gas volume mixing ratios.

Spatiotemporal Variations in Particulate Matter and Air Quality over China: National, Regional and Urban Scales

Ambient exposure to particulate matter (PM) air pollution is known to have an adverse effect on public health worldwide. Rapid increase rates of economic and urbanization, industrial development, and environmental change in China have exacerbated the occurrence of air pollution. This study examines the temporal and spatial distribution of PM on national, regional and local scales in China during 2014–2016. The relationships between the PM2.5 concentration rising rate (PMRR) and meteorological parameters (wind speed and wind direction) are discussed. The dataset of Air Quality Index (AQI), PM10 (PM diameter < 10 μm ) and PM2.5 (PM diameter < 2.5 μm) were collected in 169, 369, and 367 cities in 2014, 2015, and 2016 over China, respectively. The results show that the air quality has been generally improved on the national scale, but deteriorated locally in areas such as the Feiwei Plain. The northwest China (NW) and Beijing-Tianjin-Hebei (BTH) regions are the worst areas of PM pollution, which are mainly manifested by the excessive PM10 caused by blowing dust in spring in NW and the intensive emissions of PM2.5 in winter in BTH. With the classified seven geographic regions, we demonstrate the significant spatial difference and seasonal variation of PM concentration and PM2.5/PM10 ratio, which indicate different emission sources. Furthermore, the dynamic analysis of the PM2.5 pollution process in 11 large urban cities shows dramatic effects of wind speed and wind direction on the PM2.5 loadings.

Temporal variability measurements of PM2.5 and its associated metals and microorganisms on a suburban atmosphere in the central Iberian Peninsula

A novel and multidisciplinary observational analysis of atmospheric components in the Central Iberian Peninsula is presented here. PM_2.5 concentrations and both populations of cultivable and non-cultivable microorganisms and concentrations of a wide range of trace elements associated have been simultaneously studied during multiple events along one year. The aim has been to characterize their potential relations and dependencies, and their seasonal, daily and hourly evolution. Tools that could explain the atmospheric mechanisms and sources from all these elements have been also evaluated. As it would be expected from a suburban environment, absolute levels obtained were not close to legislation limits. Anthropogenic and natural sources, such as heating home, soil resuspension, or Sahara dust intrusion; and atmospheric factors are responsible for higher PM_2.5 and metals concentrations in months with both low and high temperatures. Daily and hourly evolution depends on University Campus activity, especially on traffic flow and resuspended dust due to human transit. No statistical significant differences on daily or seasonal scales between cultivable counts of fungi and bacteria were displayed. However, using the q-PCR technique, the bacterial population was lower in winter. Positive correlations between PM_2.5 and relative humidity; and PM_2.5 and cultivable microorganism have been established. It was also the case among 7 of the 11 trace elements, indicating then common natural or anthropogenic sources. In summary, this work illustrates the interest of a combined inspection of elements, interactions and dependencies when studying the unique and continuous atmospheric environment, which are typically analysed separately.

Research on PM2.5 concentration based on dissipative structure theory: a case study of Xi'an, China

PM_2.5 pollution has become a serious urban environmental problem, especially in developing countries with increasing urbanization. Understanding the proportion of PM_2.5 generation sources has laid a foundation for better PM_2.5 concentration reduction This paper used Point of Interesting (POI)data, building profile data of Xi'an, PM_2.5 concentration and wind monitoring data of five provinces near Xi'an as the basic data. And this paper studied the spatial distribution of various buildings in Xi'an, the temporal and spatial distribution of PM_2.5 in Xi'an and the five provinces, and found that the spatial distribution of PM_2.5 concentration in Xi'an and the building distribution in Xi'an does not match. Based on this, a quantitative model of PM_2.5 concentration in Xi'an, energy consumption, wind, and other factors is established through the qualitative and quantitative analysis of PM_2.5 concentration in Xi'an. Entropy theory and dissipative structure theory are applied to analyze this phenomenon. The results show PM_2.5 in Xi'an mainly comes from the spread of PM_2.5 in the five provinces. The PM_2.5 generated by energy consumption in Xi'an is not enough to cause serious PM_2.5 pollution. And further suggestions on how to reduce PM_2.5 concentration in Xi'an are put forward.

The heterogeneous effect of socioeconomic driving factors on PM2.5 in China's 30 province-level administrative regions: Evidence from Bayesian hierarchical spatial quantile regression

China has become one of the most serious PM_2.5-dominated air pollution country. Despite a great deal of research has focused on analysing the influence of social and economic driving forces of PM_2.5 pollution in China, most research in existence either applying mean regression or failing to consider the spatial autocorrelation. Motivated by this, this paper utilizes a Bayesian hierarchical spatial quantile regression method to explore the effect of socioeconomic activity on PM_2.5 air pollution. By introducing spatial random effects into the model, the spatial autocorrelations of residuals are significantly reduced. The empirical study demonstrated that the PM_2.5 concentration levels were strongly correlated with total population, urbanization rate, industrialization level and energy efficiency at all quantiles. For upper quantiles, the impact of urbanization rate on the haze is the greatest among all the predictors, then followed by the total population; while for lower quantiles, industrialization has the greatest impact on the PM_2.5 concentration. The impacts of energy efficiency in the lower 15% and upper 15% quantiles are higher compared to any of the other quantiles.

Cause of PM2.5 pollution during the 2016-2017 heating season in Beijing, Tianjin, and Langfang, China

To investigate the cause of fine particulate matter (particles with an aerodynamic diameter less than 2.5 µm, PM_2.5) pollution in the heating season in the North China Plain (specifically Beijing, Tianjin, and Langfang), water-soluble ions and carbonaceous components in PM_2.5 were simultaneously measured by online instruments with 1-hr resolution, from November 15, 2016 to March 15, 2017. The results showed extreme severity of PM_2.5 pollution on a regional scale. Secondary inorganic ions (SNA, i.e., NO₃^-+SO₄²+ NH₄⁺) dominated the water-soluble ions, accounting for 30%-40% of PM_2.5, while the total carbon (TC, i.e., OC + EC) contributed to 26.5%-30.1% of PM_2.5 in the three cities. SNA were mainly responsible for the increasing PM_2.5 pollution compared with organic matter (OM). NO₃^- was the most abundant species among water-soluble ions, but SO₄^2- played a much more important role in driving the elevated PM_2.5 concentrations. The relative humidity (RH) and its precursor SO₂ were the key factors affecting the formation of sulfate. Homogeneous reactions dominated the formation of nitrate which was mainly limited by HNO₃ in ammonia-rich conditions. Secondary formation and regional transport from the heavily polluted region promoted the growth of PM_2.5 concentrations in the formation stage of PM_2.5 pollution in Beijing and Langfang. Regional transport or local emissions, along with secondary formation, made great contributions to the PM_2.5 pollution in the evolution stage of PM_2.5 pollution in Beijing and Langfang. The favourable meteorological conditions and regional transport from a relatively clean region both favored the diffusion of pollutants in all three cities.

Changes in spatial patterns of PM2.5 pollution in China 2000-2018: Impact of clean air policies

To improve air quality, China has been implementing strict clean air policies since 2013. These policies not only substantially improved air quality but may also modify the spatial distribution of air pollution, since urban emission sources were under stricter control and some were moved to rural regions with lower air quality improvement targets and lacking of monitoring. Here, we predicted satellite-based monthly PM_2.5 concentrations during 2000-2018 at a 1-km resolution with complete spatial-temporal coverage to analyze changes in the spatial pattern of PM_2.5 pollution in China. We found that the PM_2.5 concentration in urban regions was higher than that in rural regions of the same city by an average of 3.3 μg/m³ during 2000-2018. This urban-rural disparity in PM_2.5 concentration significantly increased from 2.5 μg/m³ in 2000 and peaked in 2007 of 3.8 μg/m³, then it sharply declined by 49% during 2013-2018 with the implementation of clean air policies. This shrinkage in the urban-rural PM_2.5 gap was partly due to the 1.3 μg/m³ greater average decrease in the PM_2.5 level in the urban region than in the rural region of the same town during 2013-2018 on average. We also observed that cities that started monitoring earlier experienced greater decreases in the urban-rural PM_2.5 difference, and regions surrounding monitor showed significantly greater PM_2.5 decrease than regions far away from monitor during 2013-2018. Additionally, clean air policies modified the relationship between PM_2.5 concentrations and per capita gross domestic product (GDP), leading to a lower PM_2.5 level with the same per capita GDP after 2013. Emissions in rural and suburban regions should be considered to further improve air quality in China.

Measuring the Urban Particulate Matter Island Effect with Rapid Urban Expansion

Rapid urbanization has posed numerous negative impacts on the environment, including fine particulate matter (PM_2.5) pollution. However, quantitative investigations of the PM_2.5 concentration trends over an urban-rural gradient at the local level are still lacking. The urban particulate matter island (UPI) effect, representing the phenomenon that high particle concentrations in urban areas are gradually attenuated to surrounding areas, was adopted and modified in this paper to study the Hangzhou Bay area from 2000 to 2015. We found the following: (1) every urban area in the Hangzhou Bay area experienced rapid expansion, especially during 2000–2005; (2) more than half of the urban areas suffered UPI problems, and these urban areas had relatively high and stable UPI intensity (UPII) values, although the UPI footprint (UPIFP) values decreased with urban expansion; and (3) urban areas could be divided into three categories: plain areas, hilly areas and the junction of plains and hills, and the probability of the UPI effect varied significantly for different categories. This paper can compensate for the lack of research on the UPI effect at the local level and provide scientific evidence for air pollution control during urban agglomeration planning.

Characterization of particulate matter and its extinction ability during different seasons and weather conditions in Sinkiang, China: two case studies

The Sinkiang Uygur Autonomous Region is located in western China and the centre of the Asian-European continent, which frequently suffers from sandstorm disasters and haze weathers. A 1-year measurement campaign in two selected points in Shihezi and Urumqi of Sinkiang was conducted to characterise the effects of particle matters and factors on particle extinction under different weather conditions. Results showed that the average concentration of PM_1-2.5 was 19.83, 9.230, 28.93 and 122.4 μg/m³ in Shihezi and 67.25, 16.80, 59.19 and 324.0 μg/m³ in Urumqi for spring, summer, autumn and winter, respectively. PM_5-10 concentrations can reach up to 500.0 and 160.0 μg/m³ during polluted weather conditions in Shihezi and Urumqi, respectively. PM_5-10 also accounted for the largest fraction in Shihezi and Urumqi for all types of weather, reaching up to 40.0% under dusty weather conditions. PM_1-2.5 significantly increased during winter possibly due to the increased demand for heating compared with non-heating periods. PM_0.5-1 is possibly produced from motor vehicle exhaust. Particle size is concluded to be the dominant factor for particle extinction capability under fine weather based on calculations of aerosol optical properties. The refractive index for a particle with a diameter of 100 μm (3.10-3.11i) is substantially larger than that with a diameter of 0.5000 μm (1.600-0.07000i), but the extinction capability of the latter is 1.30 times that of the former. Comparatively, when the mass concentration of coarse particles is over 17.0 times that of the fine particles, and then mass concentration becomes the dominant factor. Therefore, visibility is negatively correlated to particle mass variations during dust storms and hazy days but not for fine days.

Spatial-temporal variability of PM2.5 air quality in Beijing, China during 2013-2018

This study investigates spatial-temporal variability and trends of ambient PM_2.5 in Beijing, China, using data collected from eight urban and four suburban stations. During 2013-2018, the city-wide annual PM_2.5 concentrations decreased significantly by 40% (84 μg/m³ in 2013 vs. 50 μg/m³ in 2018). The decreasing PM_2.5 trend is more pronounced in winter and during the heating season (November-March), in urban areas, and at the median and upper percentiles of PM_2.5 concentrations. The 95th percentile PM_2.5 concentrations had decreased by 20 μg/m³/yr in the heating season and 16 μg/m³/yr in the non-heating season. During the six-year study period, there was a significant increase in excellent air quality days (PM_2.5 concentration < 35 μg/m³) and a significant decrease in heavy pollution days (PM_2.5 concentration > 150 μg/m³). PM_2.5 concentrations were strongly correlated across the 12 stations. Urban areas in south Beijing experienced higher PM_2.5 levels than suburban sites at every hour-of-day, day-of-week, and month-of-year. PM_2.5 levels were higher during winter and the heating season, when PM_2.5 emission was high due to space heating and mixing layer heights were low. PM_2.5 was higher at weekends than during weekdays, when 20% of private passenger vehicles are prohibited, and higher at night than during the day, when heavy duty delivery vehicles are not permitted. These temporal and spatial trends suggest that Beijing's PM_2.5 is strongly impacted by local emissions. Our results indicate, control strategies implemented were successful in Beijing's air quality improvement, but further reduction of PM_2.5 concentrations in Beijing could be challenging due to significant contribution from its neighboring cities, calling for comprehensive and collaborative efforts in regional/national scale.

Spatiotemporal disequilibrium and spillover effect of fine particulate matter across China

Massive monitoring data requires effective statistical analysis methods. This paper aims to visualize the spatiotemporal characteristics and spillover effect of air pollutants. Ground-based PM_2.5 data in 336 cities across China revealed a tough but improving situation. The PM_2.5 average annual concentrations in 2016 and 2017 were 47 ± 18 µg/m³ and 44 ± 16 µg/m³ respectively, but a worse, or at least a not-improving PM_2.5 situation happened in winter. A slight declining north-south disequilibrium and a growing east-west disequilibrium exhibited in 2017, along with an increasing weight of eastern and southern pollution in the proportion of the overall pollution level. North-south disequilibrium existed stably throughout the year but east-west disequilibrium was erratic. Nearly half of the cities exhibited significant spillover effects, presenting 2 clusters with spillover by high concentrations and 3 clusters with spillover by low concentrations. Most cities in Hebei, Shandong and Henan provinces showed a high but decreasing spillover effect, but increasing trend happened in the cities in Anhui and Shanxi provinces. A significant correlation appeared between the city population and PM_2.5 concentration. Population density explained about 25% of the PM_2.5 concentration change, and the explanation ability increased in 2017. A higher influence of population on PM_2.5 concentration happened in the early stage of city development, and the influence exhibited spatial differences. The city population and PM_2.5 spillover effect existed an overall positive correlation, but the population only addressed about 10% of the spillover effect change. Our findings provide important information for the joint prevention and control of air pollution in China, and the approach proposed in this paper is applicable to other fields.

Pareto law-based regional inequality analysis of PM2.5 air pollution and economic development in China

Regional inequality has caused large social and economic problems in China. Numerous researchers have sought to understand the status of economic inequality in the past decades. However, studies are lacking on other aspects of regional inequality, particularly when multiple facets must be considered. In this study, we have innovatively proposed a Pareto law-based method that can help assess multiple dimensions of regional inequality simultaneously. With this approach, we can rank multiple aspects of inequality and provide robust, reasonable goals for different groups of administrative districts. The proposed approach was successfully implemented by using Chinese data for 2015 and 2016, a period during which China was experiencing both severe PM_2.5 pollution and economic regional inequality. The results indicate that (1) Shanghai and Shenzhen represent the optimal condition of economic development; (2) different from the spatial distribution of economic inequality alone, inequality was higher in central China for both economic development and PM_2.5 air quality; (3) in the context of severe economic inequality in China, the tradeoff between economic development and air quality will result in a relatively equitable condition. In addition, the proposed method is open-ended and can be extended to incorporate more aspects of regional inequality. This approach appears to possess substantial potential for integration into decision-making regarding regional inequality.

Impacts of hazardous metals and PAHs in fine and coarse particles with long-range transports in Taipei City

This study assessed the impact on air quality and health risk by long-range transported (LRT) PM_2.5-10- and PM_2.5-bound metals and PAHs in Taipei City, Taiwan. Several methods with receptor aerosol measurements were used to quantify the effect of LRT. The hybrid single particle lagrangian integrated trajectory model (HYSPLIT) was used in conjunction with the potential source contribution function (PSCF) to distinguish the LRT aerosols. By using a general linear model (GLM) with a marginal mean and positive matrix fraction (PMF), this study also evaluated the annual increased level of LRT (AIR_LRT) for each source contribution to the concentration and the resultant health risk of particle-bound metals and polycyclic aromatic hydrocarbons (PAHs). The LRT influenced fine-sized metals and PAHs rather than coarse-sized ones. We found that the level of PM_2.5-bound toxic metals (Pb, Cd, and As) and PAHs (Benzo[a]pyrene and dibenzo[a,e]pyrene) could increase by 90% under the influence of LRT in 2014, while an AIR_LRT value of 25% for the PM_2.5 mass concentration was observed. Overall, the excess cancer risk (ECR) resulting from PM_2.5-bound metal and PAH exposures was 6.40 × 10^-5 in relation to coal combustions (20.7%), traffic-related emissions (59.7%) and re-suspended aerosols (19.6%). Among these contributors, LRT-related metals and PAHs in PM_2.5 accounted for 51% of the total ECR.

Evidence of the mitigated urban particulate matter island (UPI) effect in China during 2000-2015

Urban fine particulate matter (PM_2.5) pollution has been the subject of great concern, due to its remarkable adverse effects on public health. However, quantitative investigations of the spatial concentration trends from urban to background areas are still lacking. The urban particulate matter island (UPI) effect, referring to the phenomenon that higher particle concentrations in urban areas are gradually attenuated to background areas, is found and investigated in this study. UPI intensity (UPII) and its footprint (UPIFP) are defined to quantify the magnitude and extent of UPI, respectively. Based on observations from 338 Chinese prefectures for 2000-2015, we confirm the existence of the UPI effect, and further reveal its spatiotemporal patterns. We find that: 1) 84% (283/338) of the cities in China in various city levels and climatic zones showed the UPI phenomenon during 2000-2015, and this phenomenon is closely related to the land-use/cover patterns between the urban area and surrounding areas; 2) different spatial patterns of UPI effect are apparent, with high UPII values and small UPIFP values in Beijing-Tianjin-Hebei, moderate UPII values and large UPIFP values in northern China, the Pearl River Delta and the Yangtze River Delta, and high UPII and UPIFP values in the Western Taiwan Straits region; 3) UPI mitigation can be observed nationwide, with significant decreasing trends for both UPII and UPIFP, benefiting from the increase in urban green spaces and the built-up proportion differences between urban and suburban areas during urbanization. Additionally, it is indicated that more urban residents and faster urban expansion correspond to a steeper decline of UPII in China during 2000-2015. The existence and characteristics of the UPI effect in China will allow new insight and understanding of urban pollution patterns, and will provide scientific evidence for urban planning and pollution control.

Spatial Relationships between Urban Structures and Air Pollution in Korea

Urban structures facilitate human activities and interactions but are also a main source of air pollutants; hence, investigating the relationship between urban structures and air pollution is crucial. The lack of an acceptable general model poses significant challenges to investigations on the underlying mechanisms, and this gap fuels our motivation to analyze the relationships between urban structures and the emissions of four air pollutants, including nitrogen oxides, sulfur oxides, and two types of particulate matter, in Korea. We first conduct exploratory data analysis to detect the global and local spatial dependencies of air pollutants and apply Bayesian spatial regression models to examine the spatial relationship between each air pollutant and urban structure covariates. In particular, we use population, commercial area, industrial area, park area, road length, total land surface, and gross regional domestic product per person as spatial covariates of interest. Except for park area and road length, most covariates have significant positive relationships with air pollutants ranging from 0 to 1, which indicates that urbanization does not result in a one-to-one negative influence on air pollution. Findings suggest that the government should consider the degree of urban structures and air pollutants by region to achieve sustainable development.

Lung cancer and particulate pollution: A critical review of spatial and temporal analysis evidence

BACKGROUND

Particulate matter (PM) has been recognized as one of the key risk factors of lung cancer. However, spatial and temporal patterns of this association remain unclear. Spatiotemporal analyses incorporate the spatial and temporal structure of the data within random effects models, generating more accurate evaluations of PM-lung cancer associations at a scale that can better inform lung cancer prevention programs.

METHODS

We conducted a critical review of spatial and temporal analyses of PM and lung cancer. The databases of PubMed, Web of Science and Scopus were searched for potential articles published until September 30, 2017. We included studies that applied spatial and temporal analyses to evaluate the associations of PM_2.5 (inhalable particles with diameters that are 2.5 µm and smaller) and PM₁₀ (inhalable particles with diameters that are 10 µm and smaller) with lung cancer.

RESULTS

We identified 17 articles eligible for the review. Of these, 11 focused on PM_2.5, five on PM₁₀, and one on both. These studies suggested a significant positive association between PM_2.5 exposure and the risk of lung cancer. Relative risks of lung cancer mortality ranged from 1.08 (95% confidence interval (CI): 1.07-1.09) to 1.60 (95%CI: 1.09-2.33) for 10 µg/m³ increase in PM_2.5 exposure. The association between PM₁₀ and lung cancer had been less well researched and the results were not consistent. In terms of the analysis methods, 16 papers undertook spatial analysis and one paper employed temporal analysis. No paper included spatial and temporal analyses simultaneously and considered spatiotemporal uncertainty into model predictions. Among the 16 papers with spatial analyses, thirteen studies presented maps, while only five and 11 studies utilized spatial exploration and modeling methods, respectively.

CONCLUSIONS

Advanced spatial and temporal epidemiological methods were seldom applied to PM-lung cancer associations. Further research is urgently needed to develop and employ robust and comprehensive spatiotemporal analysis methods for the evaluation of PM-lung cancer associations and the support of lung cancer prevention strategies.