Spatial Variation Of PM2 Research Articles

Study on the Temporal and Spatial Variation of PM2.5 Health Burden in Henan Province from 2014 to 2021

Study on the Temporal and Spatial Variation of PM2.5 Health Burden in Henan Province from 2014 to 2021

Within-city spatial variations in PM2.5 magnetite nanoparticles and brain cancer incidence in Toronto and Montreal, Canada.

Magnetite nanoparticles are small, strongly magnetic iron oxide particles which are produced during high-temperature combustion and friction processes and form part of the outdoor air pollution mixture. These particles can translocate to the brain and have been found in human brain tissue. In this study, we estimated associations between within-city spatial variations in concentrations of magnetite nanoparticles in outdoor fine particulate matter (PM2.5) and brain cancer incidence. We performed a cohort study of 1.29 million participants in four cycles of the Canadian Census Health and Environment Cohort in Montreal and Toronto, Canada who were followed for malignant brain tumour (glioma) incidence. As a proxy for magnetite nanoparticle content, we measured the susceptibility of anhysteretic remanent magnetization (χARM) in PM2.5 samples (N = 124 in Montreal, N = 110 in Toronto), and values were assigned to residential locations. Stratified Cox proportional hazards models were used to estimate hazard ratios (per IQR change in volume-normalized χARM). ARM was not associated with brain tumour incidence (HR = 0.998, 95% CI 0.988, 1.009) after adjusting for relevant potential confounders. Although we found no evidence of an important relationship between within-city spatial variations in airborne magnetite nanoparticles and brain tumour incidence, further research is needed to evaluate this understudied exposure, and other measures of exposure to magnetite nanoparticles should be considered.

Investigation of the spatial effects on PM2.5 in relation to land use and ecological restoration in urban agglomerations

Heavy pollution of particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) poses increasing threats to the living environment worldwide. Urban agglomerations often lead to regional rather than local air pollution problems. This study explored the underlying global and local spatial driving mechanisms of PM2.5 variations of the 195 county-level administrative units in the urban agglomeration in the middle reaches of the Yangtze River, China, in 2020, using the global spatial regression and geographically weighted regression methods. Results showed that (1) at the county level, there were spatial variations of PM2.5, fluctuating from 20.1263 μg/m3 to 44.8416 μg/m3. (2) The concentrations of PM2.5 presented a positive spatial autocorrelation with a remarkable direct spatial spillover effect. (3) Forestland, grassland, elevation and ecological restoration were negatively correlated with PM2.5 concentrations, the indirect spatial spillover effect of elevation was noticeable. (4) The indirect reduction effects of ecological restoration on PM2.5 concentrations were substantial in the Wuhan urban agglomeration. (5) The reduction effect of forestland, grassland, ecological restoration and elevation on PM2.5 showed a noticeable spatial heterogeneity. In the future, it is suggested regional variability and the spatial spillover effect of air pollution be taken into account in environmental governance. Simultaneously, utilization of the mitigation effect of ecological restoration on PM2.5 is anticipated for the concerted effort in air pollution governance.

Spatial variation of PM2.5 component concentrations in 23 Tokyo wards

Spatial variation of PM2.5 component concentrations in 23 Tokyo wards

Developing high-resolution PM2.5 exposure models by integrating low-cost sensors, automated machine learning, and big human mobility data

Introduction: Traditional methods to estimate exposure to PM2.5 (particulate matter with less than 2.5 µm in diameter) have typically relied on limited regulatory monitors and do not consider human mobility and travel. However, the limited spatial coverage of regulatory monitors and the lack of consideration of mobility limit the ability to capture actual air pollution exposure.Methods: This study aims to improve traditional exposure assessment methods for PM2.5 by incorporating the measurements from a low-cost sensor network (PurpleAir) and regulatory monitors, an automated machine learning modeling framework, and big human mobility data. We develop a monthly-aggregated hourly land use regression (LUR) model based on automated machine learning (AutoML) and assess the model performance across eight metropolitan areas within the US.Results: Our results show that integrating low-cost sensor with regulatory monitor measurements generally improves the AutoML-LUR model accuracy and produces higher spatial variation in PM2.5 concentration maps compared to using regulatory monitor measurements alone. Feature importance analysis shows factors highly correlated with PM2.5 concentrations, including satellite aerosol optical depth, meteorological variables, vegetation, and land use. In addition, we incorporate human mobility data on exposure estimates regarding where people visit to identify spatiotemporal hotspots of places with higher risks of exposure, emphasizing the need to consider both visitor numbers and PM2.5 concentrations when developing exposure reduction strategies.Discussion: This research provides important insights for further public health studies on air pollution by comprehensively assessing the performance of AutoML-LUR models and incorporating human mobility into considering human exposure to air pollution.

Spatial Variation of PM2.5 Indoors and Outdoors: Results from 261 Regulatory Monitors Compared to 14,000 Low-Cost Monitors in Three Western States over 4.7 Years.

Spatial variation of indoor and outdoor PM2.5 within three states for a five-year period is studied using regulatory and low-cost PurpleAir monitors. Most of these data were collected in an earlier study (Wallace et al., 2022 Indoor Air 32:13105) investigating the relative contribution of indoor-generated and outdoor-infiltrated particles to indoor exposures. About 260 regulatory monitors and ~10,000 outdoor and ~4000 indoor PurpleAir monitors are included. Daily mean PM2.5 concentrations, correlations, and coefficients of divergence (COD) are calculated for pairs of monitors at distances ranging from 0 (collocated) to 200 km. We use a transparent and reproducible open algorithm that avoids the use of the proprietary algorithms provided by the manufacturer of the sensors in PurpleAir PA-I and PA-II monitors. The algorithm is available on the PurpleAir API website under the name "PM2.5_alt". This algorithm is validated using several hundred pairs of regulatory and PurpleAir monitors separated by up to 0.5 km. The PM2.5 spatial variation outdoors is homogeneous with high correlations to at least 10 km, as shown by the COD index under 0.2. There is also a steady improvement in outdoor PM2.5 concentrations with increasing distance from the regulatory monitors. The spatial variation of indoor PM2.5 is not homogeneous even at distances < 100 m. There is good agreement between PurpleAir outdoor monitors located <100 m apart and collocated Federal Equivalent Methods (FEM).

Spatiotemporal Patterns and Characteristics of PM2.5 Pollution in the Yellow River Golden Triangle Demonstration Area

Improving air quality in the Yellow River Golden Triangle Demonstration Area (YRGTDA) is an important practice for ecological protection and high-quality development in the Yellow River Basin. Preventing and controlling PM2.5 pollution in this region will require a scientific understanding of the spatiotemporal patterns and characteristics of PM2.5 pollution. PM2.5 data from different sources were combined in this study (the annual average of PM2.5 concentrations were obtained from the Atmospheric Composition Analysis Group of Dalhousie University, and the daily PM2.5 concentration data were obtained from the China National Environmental Monitoring Centre). Then, the temporal variation of PM2.5 concentrations at annual, seasonal, and monthly scales, the spatial variation of PM2.5 concentrations, and the variation of PM2.5 pollution classes were analyzed. Results showed that: (1) at the annual scale, the PM2.5 concentrations showed a decreasing trend from 2000 to 2021 in the study area. The variation of PM2.5 concentrations were divided into two different stages. (2) At the seasonal scale, high PM2.5 concentrations occurred mainly in winter, low PM2.5 concentrations occurred in summer. At the monthly scale, PM2.5 concentrations showed a U-shaped variation pattern from January to December each year. (3) The hotspot analysis of the PM2.5 concentrations in the study area showed a cyclical variation pattern. (4) The PM2.5 concentrations exhibited a spatial pattern of high values in the central and low values in the northern and southern parts of YRGTDA. (5) The number of days for different PM2.5 pollution classes from 2015 to 2021 followed the order of Good &gt; Excellent &gt; Light pollution &gt; Moderate pollution &gt; Heavy pollution &gt; Severe pollution in YRGTDA. The results of this study have great theoretical and practical significance because they reveal the spatiotemporal patterns and pollution characteristics of PM2.5 and will lead to the development of scientifically based measures to reasonably prevent and control pollution in YRGTDA.

Spatio-temporal Variation in PM2.5 Concentration and Its Relationship with Vegetation Landscape Patterns in Typical Economic Zones in China from 2000 to 2020

This study explored the temporal and spatial variation in PM2.5 concentration and its relationship with the vegetation landscape pattern in three typical economic zones in China, which is of great significance for regional PM2.5pollution control and atmospheric environmental protection. In this study, the pixel binary model, Getis-Ord Gi* analysis, Theil-Sen Median analysis, Mann-Kendall significance test, Pearson correlation analysis, and multiple correlation analysis were used to explore the spatial cluster and spatio-temporal variation in PM2.5 and its correlation with the vegetation landscape index in the three economic zones of China on the basis of PM2.5 concentration data and MODIS NDVI data set. The results showed that PM2.5 in the Bohai Economic Rim was mainly dominated by the expansion of hot spots and the reduction in cold spots from 2000 to 2020. The proportion of cold spots and hot spots in the Yangtze River Delta showed insignificant changes. Both cold and hot spots in the Pearl River Delta had expanded. PM2.5 showed a downward trend in the three major economic zones from 2000 to 2020, and the magnitudes of increasing rates were higher in the Pearl River Delta, followed by those in the Yangtze River Delta and Bohai Economic Rim. From 2000 to 2020, PM2.5 exhibited a downward trend in the context of all vegetation coverage grades, and PM2.5 had most significantly improved within extremely low vegetation coverage in the three economic zones. On the landscape scale, PM2.5 values were mostly correlated with aggregation index in the Bohai Economic Rim, with the largest patch index in the Yangtze River Delta and Shannon's diversity in the Pearl River Delta, respectively. Under the context of different vegetation coverage levels, PM2.5showed the highest correlation with aggregation index in the Bohai Economic Rim, landscape shape index in the Yangtze River Delta, and percent of landscape in the Pearl River Delta, respectively. PM2.5 showed significant differences with vegetation landscape indices in the three economic zones. The combined effect of multiple vegetation landscape pattern indices on PM2.5 was stronger than that of the single vegetation landscape pattern index. The above results indicated that the spatial cluster of PM2.5 in the three major economic zones had changed, and PM2.5 showed a decreasing trend in the three economic zones during the study period. The relationship between PM2.5 and vegetation landscape indices exhibited obvious spatial heterogeneity in the three economic zones.

Can urban polycentricity improve air quality? Evidence from Chinese cities

This study utilizes high-resolution population grid data from 2004 to 2018 to assess the extent of urban polycentricity in 284 Chinese prefecture-level cities. A new method for measuring urban polycentricity is devised using the Clauset-Newman-Moore grey modularity maximization algorithm. Additionally, the Spatial Durbin Model is applied to investigate the effect of urban polycentricity on the mean and spatial variation of PM2.5 concentrations. Our statistical results show that the increased distance between urban centers and the increased size disparity between the large and small centers effectively reduces air pollution within the city. On the other hand, in cities with a strong clustering of urban centers and significant size disparity between main and sub-centers, the polycentric urban structure cannot help reduce air pollution but merely shift it to other urban centers within the city. Hence, interpreting the severity of urban air pollution solely on the basis of the citywide average of air pollution may be insufficient. It is necessary to consider the possible relocation of air-polluting activities associated with polycentric urban structures. Policymakers should take into account the potential spatial inequality of air pollution driven by urban polycentricity.

Spatially heterogeneous relationships of PM2.5 concentrations with natural and land use factors in the Niger River Watershed, West Africa

Identifying the main factors influencing PM2.5 concentrations is critical for the control and management of elevated atmospheric pollution. Although the impact of natural and land use factors on PM2.5 concentration has previously been quantified, in this paper we show that multiple environmental factors exhibit significant spatial heterogeneity at a range of scales. A more comprehensive analysis of PM2.5 variations require the potential effects of seasonal variations in meteorological conditions, interactions between natural and land use factors, and possible multi-scale spatial effects to be accounted for. Based on the global remote sensing inversion dataset, the aim of this study is to elucidate the spatially heterogeneous relationships of PM2.5 concentrations with natural and land use factors, and their multi–scale effects. This is achieved through combining geographical detector (GD), random forest (RF) and multiscale geographically weighted regression modelling (MGWR) to reveal the underlying mechanisms of spatial variation of PM2.5 concentrations in the Niger River Watershed (NRW). The results show that natural factors, represented by meteorological and topography variables, are most prominent. Human activities, represented by land use, have significant indirect effects through interactions with meteorological factors. Elevation (ELE) and annual minimum temperature (TMIN) emerge as the most important interaction centers among natural factors. In addition, the interaction between natural and land use factors are crucial in considering PM2.5 transmission and dispersion in the NRW. The topography and meteorological factors underlie the basic spatial pattern PM2.5 concentrations in the NRW, among which seasonal meteorological parameters have a more prominent impact on PM2.5 concentration. Both natural and land use factors exhibit strong non–stationarity in respect of spatial variations in PM2.5, and the effect of environmental factors in general on PM2.5 is scale–dependent. Transport and dispersion are important constraints in terms of aerosol loading regionally. The study suggests that coupling processes between meteorological conditions, land use and PM2.5 concentrations, based on the scale effects of each factor, should be accounted for in developing aerosol pollution regulations and cooperative management strategies in the region.

Spatial Variability of PM2.5 Pollution in Imbalanced Natural and Socioeconomic Processes: Evidence from the Beijing-Tianjin-Hebei Region of China

Spatial Variability of PM2.5 Pollution in Imbalanced Natural and Socioeconomic Processes: Evidence from the Beijing-Tianjin-Hebei Region of China

Exploring the effect of waterbodies coupled with other environmental parameters to model PM2.5 over Delhi-NCT in northwest India

The problem of air pollution, especially, the concentration of PM2.5 is a long-standing issue over the national capital territory of India (Delhi- NCT). This study seeks to explore the effect of surface waterbody on the spatial variation of PM2.5 in Delhi-NCT. As moisture determines the concentration of PM2.5 and other pollutants in the atmosphere, the presence of surface waterbody plays a vital role in controlling PM2.5 in the local environment. We use distance to waterbodies, including other covariates of meteorology and surface characteristics, such as the surface greenness and land cover type, to model near the surface concentration of PM2.5. Different machine learning (ML) approaches were applied to predict the spatio-temporal behavior of PM2.5. Out of different ML-based models, the random forest regression produces the highest predictive accuracy (R2 = 0.68), while, gradient boosting, support vector machine and artificial neural net have R2 less than 0.66. The outcome of the feature important score from random forest regression reveals that proximity to the water surface is more important than precipitation and land use and land cover (LULC). The results find that up to a distance of 3 km, the waterbody exerts a significant effect in lowering PM2.5 concentration. The findings suggest that restoring the surface waterbodies – moderate to large in size, including the other prescribed measures could potentially restore the air quality standards for the Delhi-NCT and elsewhere in the world where particulate matter pollution is of great concern.

Dependence Analysis of PM2.5 Concentrations in 295 Chinese Cities in the Winter of 2019–2020

Considering the current severe atmospheric pollution problems in China, a comprehensive understanding of the distribution and spatial variability of PM2.5 is critically important for controlling pollution and improving the future atmospheric environment. This study first explored the distribution of PM2.5 concentrations in China, and then developed a methodology of “dependence analysis” to investigate the relationship of PM2.5 in different cities in China. The data of daily PM2.5 concentrations were collected from the environmental monitoring stations in 295 cities in China. This study also developed a set of procedures to evaluate the spatial dependence of PM2.5 among the 295 Chinese cities. The results showed that there was a total of 154 city pairs with dependence type “11”, under a significance level of 0.5%. Dependence type “11” mainly occurred between nearby cities, and the distance between 89.0% of the dependent city pairs was less than 200 km. Furthermore, the dependent pairs mainly clustered in the North China Plain, the Northeast Plain, the Middle and Lower Yangtze Plain and the Fen-Wei Plain. The geographic conditions of the Plain areas were more conducive to the spread of PM2.5 contaminants, while the mountain topography was unfavorable for the formation of PM2.5 dependencies. The dependent city couples with distances greater than 200 km were all located within the Plain areas. The high concentration of PM2.5 did not necessarily lead to PM2.5 dependences between city pairs. The methodology and models developed in this study will help explain the concentration distributions and spatial dependence of the main atmospheric pollutants in China, providing guidance for the prevention of large-scale air pollution, and the improvement of the future atmospheric environment.

Effects of anthropogenic precursor emissions and meteorological conditions on PM2.5 concentrations over the “2+26” cities of northern China

Elucidating the characteristics and influencing mechanisms of PM2.5 concentrations is the premise and key to the precise prevention and control of air pollution. However, the temporal and spatial heterogeneity of PM2.5 concentrations and its driving mechanism are complex and need to be further analyzed. We analyzed the temporal and spatial variations of PM2.5 concentrations in the “2 + 26” cities from 2015 to 2021, and quantified the influence of meteorological factors and anthropogenic emissions and their interactions on PM2.5 concentrations based on geographic detector model. We find the inter-annual and inter-season PM2.5 concentrations show downward trend from 2015 to 2021, and the inter-month PM2.5 concentrations present a U-shaped distribution. The PM2.5 concentrations in the “2 + 26” cities manifest a spatial distribution pattern of high in the south and low in the north, and high in the middle and low in the surroundings. Meteorological conditions have stronger effects on PM2.5 concentrations than anthropogenic emissions, and planetary boundary layer height and temperature are the two main driving factors at the annual scale. On the seasonal scale, sunshine duration is the dominant factor of PM2.5 concentrations in summer and autumn, and planetary boundary layer height is the dominant factor of PM2.5 concentrations in winter. The effect of anthropogenic emissions on PM2.5 concentration is higher in winter and spring than in summer and autumn, and ammonia and ozone have stronger effects on PM2.5 concentrations than other anthropogenic emissions. Interactions between the factors significantly enhance the PM2.5 concentrations. The interactions between planetary boundary layer height and other impacting factors play dominant roles on PM2.5 concentrations at annual scale and in winter. Our results not only provide crucial information for further developing air quality policies of the “2 + 26” cities, but also bear out several important implications for clean air policies in China and other regions of the world.

Spatiotemporal continuous estimates of daily 1 km PM2.5 from 2000 to present under the Tracking Air Pollution in China (TAP) framework

Abstract. High spatial resolution PM2.5 data covering a long time period are urgently needed to support population exposure assessment and refined air quality management. In this study, we provided complete-coverage PM2.5 predictions with a 1 km spatial resolution from 2000 to the present under the Tracking Air Pollution in China (TAP, http://tapdata.org.cn/, last access: 3 October 2022) framework. To support high spatial resolution modeling, we collected PM2.5 measurements from both national and local monitoring stations. To correctly reflect the temporal variations in land cover characteristics that affected the local variations in PM2.5, we constructed continuous annual geoinformation datasets, including the road maps and ensemble gridded population maps, in China from 2000 to 2021. We also examined various model structures and predictor combinations to balance the computational cost and model performance. The final model fused 10 km TAP PM2.5 predictions from our previous work, 1 km satellite aerosol optical depth retrievals, and land use parameters with a random forest model. Our annual model had an out-of-bag R2 ranging between 0.80 and 0.84, and our hindcast model had a by-year cross-validation R2 of 0.76. This open-access, 1 km resolution PM2.5 data product, with complete coverage, successfully revealed the local-scale spatial variations in PM2.5 and could benefit environmental studies and policymaking.

Long-term Exposure to Oxidant Gases and Mortality: Effect Modification by PM 2.5 Transition Metals and Oxidative Potential.

Background:Populations are simultaneously exposed to outdoor concentrations of oxidant gases (i.e., O3 and NO2) and fine particulate air pollution (PM2.5). Since oxidative stress is thought to be an important mechanism explaining air pollution health effects, the adverse health impacts of oxidant gases may be greater in locations where PM2.5 is more capable of causing oxidative stress.Methods:We conducted a cohort study of 2 million adults in Canada between 2001 and 2016 living within 10 km of ground-level monitoring sites for outdoor PM2.5 components and oxidative potential. Ox exposures (i.e., the redox-weighted average of O3 and NO2) were estimated using a combination of chemical transport models, land use regression models, and ground-level data. Cox proportional hazards models were used to estimate associations between 3-year moving average Ox and mortality outcomes across strata of transition metals and sulfur in PM2.5 and three measures of PM2.5 oxidative potential adjusting for possible confounding factors.Results:Associations between Ox and mortality were consistently stronger in regions with elevated PM2.5 transition metal/sulfur content and oxidative potential. For example, each interquartile increase (6.27 ppb) in Ox was associated with a 14.9% (95% CI = 13.0, 16.9) increased risk of nonaccidental mortality in locations with glutathione-related oxidative potential (OPGSH) above the median whereas a 2.50% (95% CI = 0.600, 4.40) increase was observed in regions with OPGSH levels below the median (interaction P value <0.001).Conclusion:Spatial variations in PM2.5 composition and oxidative potential may contribute to heterogeneity in the observed health impacts of long-term exposures to oxidant gases.

Scale Effects and Regional Disparities of Land Use in Influencing PM2.5 Concentrations: A Case Study in the Zhengzhou Metropolitan Area, China

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

Simulation of PM2.5 Concentration Based on Optimized Indexes of 2D/3D Urban Form

It is well known that urban forms can affect the source distribution and diffusion process of air pollution; however, practical quantitative methods and models on alleviating urban air pollution by optimizing urban form indexes are lacking. Using Chang Sha city as an example, we quantitatively analyzed the PM2.5 concentration distribution in terms of 2D/3D urban form indexes (e.g., land use functionality, landscape pattern, and development intensity). Based on this, the urban form regulation-aided air quality optimization model (UFR-AQOM) was proposed and consequently employed to simulate the scene-dependent PM2.5 concentrations under double constraints from both the index threshold and air quality objectives. The results showed that the high value area of PM2.5 concentration in Chang Sha featured a "one axis and four nodes" spatial pattern. PM2.5 concentrations in grids with commercial or road land applications, high patch density or high Shannon index, and high land plot ratio or low sky openness were shown to be relatively higher. The development intensity indexes had the greatest impact on the spatial variation in PM2.5 concentration, followed by landscape pattern and land use functionality, and the interaction of factor indexes could significantly strengthen their own single contributions. The UFR-AQOM model, taking into account the contribution differences and interactions among different factors, could effectively simulate the spatial variation in PM2.5 concentration in urban areas (R2=0.65,RMSE=1.40 μg·m-3). In order to meet the regulations of PM2.5 standards, the overall management of landscape pattern indexes, such as the integral dimension of the perimeter surface and patch density, should be strengthened in the main urban area of Changsha. Further, the zoned optimization of PM2.5 concentrations could be implemented by controlling the urban land use indexes, such as the industrial land use ratio and water area ratio, as well as the development intensity indexes such as the land use area ratio. These research results provided support for decisions in the optimization of national territory spatial planning indexes targeting air quality improvement.

Seasonal and Spatial Variability of PM2.5 Concentration, and Associated Metal(loid) Content in the Toluca Valley, Mexico.

This study provides evidence of the seasonal and spatial variation of metal(lloid)s in particulate matter minor to 2.5 microns (PM2.5) in the Toluca Valley Metropolitan Area (TVMA), the fifth largest urban center in Mexico. Four sites were sampled between 2013 and 2014, which included urban and industrial areas, in the dry-cold (November-February) and dry-hot (March-May) seasons; PM2.5 was collected using high- and medium-volume samplers. Metal(lloid) concentrations in PM2.5 were analyzed using inductively coupled plasma‒mass spectrometry (ICP‒MS). The highest 24-hour PM2.5 concentration in the northern area was observed, and the PM2.5 concentrations were independent of the season. Five metal(lloid)s with a recovery percentage above 80% were considered to be reported (Co, Cr, Cu, Mn, and Sb). The maximum concentrations of metal(lloid)s were observed during the dry-cold season, and concentrations were up to one hundred or thousand fold with respect to the dry-hot season. The 24-hour PM2.5 and metal(lloid) concentrations exceeded national and international guidelines to protect population health.

Potential hotspot modeling and monitoring of PM2.5 concentration for sustainable environmental health in Maharashtra, India.

Modern human civilization has suffered from the disastrous impact of COVID-19, but it teaches us the lesson that the environment can restore its stability without human activity. The Government of India (GOI) has launched many strategies to prevent the situation of COVID-19, including a lockdown that has a great impact on the environment. The present study focuses on the analysis of Particulate Matter 2.5 (PM2.5) concentration levels in pre-locking, lockdown, and unlocking phases across ten major cities of Maharashtra (MH) that were the COVID hotspot of India during the COVID-19 outbreak; phase-wise and year-wise (2018–2020) hotspot analysis, box diagram and line graph methods were used to assess spatial variation in PM2.5 across MH cities. Our study showed that the PM2.5 concentration level was severe at pre-lockdown stage (January–March) and it decreased dramatically at the lockdown stage, later it also increased in its previous position at the unlocking stages, i.e., PM2.5 decreased dramatically (59%) during the lockdown period compared to the pre-lockdown period due to the shutdown of outdoor activities. It returns to its previous position due to the unlocking situation and increases (70%) compared to the lockdown period which illustrated the ups and downs of PM2.5 and ensures the position of different cities in the Air Quality Index (AQI) categories at different times. In the pre-lockdown phase, maximum PM2.5 concentration was in Navi Mumbai (NAV) (358) and Mumbai (MUM) (338), and Pune (PUN) (335) and Nashik NAS (325) subsequently, whereas at the last of the lockdown phase, it becomes Chandrapur (CHN) (82), Nagpur (NAG) (76), and Solapur (SOL) (45) subsequently. Hence, the restoration of the environment during the lockdown phase was temporary rather than permanent. Therefore, our findings propose that several effective policies of government such as relocation of polluting industries, short-term lockdown, odd–even vehicle number, installation of air purifier, and government strict initiatives are needed in making a sustainable environment.