Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013-2015, using a spatiotemporal land-use random-forest model

Residential exposure to air pollution and incidence of leukaemia in the industrial area of Viadana, Northern Italy

BACKGROUND

Exposure to air pollution has been proposed as one of the potential risk factors for leukaemia. Work-related formaldehyde exposure is suspected to cause leukaemia.

METHODS

We conducted a nested register-based case-control study on leukaemia incidence in the Viadana district, an industrial area for particleboard production in Northern Italy. We recruited 115 cases and 496 controls, frequency-matched by age, between 1999 and 2014. We assigned estimated exposures to particulate matter (PM10, PM2.5), nitrogen dioxide (NO2), and formaldehyde at residential addresses, averaged over the susceptibility window 3rd to 10th year prior to the index date. We considered potential confounding by sex, age, nationality, socio-economic status, occupational exposures to benzene and formaldehyde, and prior cancer diagnoses.

RESULTS

There was no association of exposures to PM10, PM2.5, and NO2 with leukaemia incidence. However, an indication of increased risk emerged for formaldehyde, despite wide statistical uncertainty (OR 1.46, 95%CI 0.65-3.25 per IQR-difference of 1.2 μg/m3). Estimated associations for formaldehyde were higher for acute (OR 2.07, 95%CI 0.70-6.12) and myeloid subtypes (OR 1.79, 95%CI 0.64-5.01), and in the 4-km buffer around the industrial facilities (OR 2.78, 95%CI 0.48-16.13), although they remained uncertain.

CONCLUSIONS

This was the first study investigating the link between ambient formaldehyde exposure and leukaemia incidence in the general population. The evidence presented suggests an association, although it remains inconclusive, and a potential significance of emissions related to industrial activities in the district. Further research is warranted in larger populations incorporating data on other potential risk factors.

Weather impact on ambient air pollution and its association with land use types/activities over 5,572 municipalities in Brazil

Quantify the impact of meteorological changes on air pollution levels is the aim of numerous recent studies. However, there is still a lack of investigations assessing the influence of land use/activities on the relationship between climate and air quality. In this study, we used a two-stage design to estimate the influence of land use types and activities on the association between weather changes and air pollution (PM2.5, NO2, SO2, O3) over 5572 municipalities in Brazil. To calculate the influence of recent weather change on air pollution concentration for each municipality, we used the "weather penalty" concept. This approach considers differences in linear trend coefficients between two generalized additive models. Then, using quantile regression, we estimated the effect of land use types and activities (8 variables related to transportation, energy generation, and land use) on weather-related increases in ambient air pollution. We found that an increase in PM2.5 was associated to recent weather changes in most municipalities (average increase of 0.07μg/m3per year) and a decrease in NO2 in most municipalities (average decrease of 0.0003 ppb per year). O3 and SO2 had more intense increases associated with weather changes in the North region. Our findings suggest the most robust positive associations between weather penalties on PM2.5 and areas with non-clean energy and oil refineries (average increase of 0.006μg/m3per year and 0.04μg/m3per year, respectively). We also found positive associations between Pasture areas, urban areas, and transportation and the weather penalties of this pollutant. In contrast, forest areas were negatively associated with PM2.5 penalties. We also found that oil refineries, urban areas, and transportation significantly positively influenced weather penalties for SO2 and O3. Overall, the study highlights the importance of considering the influence of land use types and activities on weather-related changes in ambient air pollution.

Ambient air pollution and hypertensive disorders of pregnancy in Rome

BACKGROUND

Ambient air pollution has been associated with hypertensive disorders of pregnancy (HDP), but few studies rely on assessment of fine-scale variation in air quality, specific subtypes and multi-pollutant exposures.

AIM

To study the impact of long-term exposure to individual and mixture of air pollutants on all and specific subtypes of HDP.

METHODS

We obtained data from 130,470 liveborn singleton pregnacies in Rome during 2014-2019. Spatiotemporal land-use random-forest models at 1 km spatial resolution assigned to the maternal residential addresses were used to estimate the exposure to particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), and ozone (O3).

RESULTS

For PM2.5, PM10 and NO2, there was suggestive evidence of increased risk of preeclampsia (PE, n = 442), but no evidence of increased risk for all subtypes of HDP (n = 2297) and gestational hypertension (GH, n = 1901). For instance, an interquartile range of 7.0 μg/m3 increase in PM2.5 exposure during the first trimester of pregnancy was associated with an odds ratio (OR) of 1.06 (95% confidence interval: 0.81, 1.39) and 1.04 (0.92, 1.17) after adjustment for NO2 and the corresponding results for a 15.7 μg/m3 increase in NO2 after adjustment for PM2.5 were 1.11 (0.92, 1.34) for PE and 0.83 (0.76, 0.90) for HDP. Increased risks for HDP and GH were suggested for O3 in single-pollutant models and for PM after adjustment for NO2, but all other associations were stable or attenuated in two-pollutant models.

CONCLUSIONS

The results of our study suggest that PM2.5, PM10 and NO2 increases the risk of PE and that these effects are robust to adjustment for O3 while the increased risks for GH and HDP suggested for O3 attenuated after adjustment for PM or NO2. Additional studies are needed to evaluate the effects of source-specific component of PM on subtypes as well as all types of HDP which would help to target preventive actions.

Particulate matter forecast and prediction in Curitiba using machine learning

Introduction

Air quality is directly affected by pollutant emission from vehicles, especially in large cities and metropolitan areas or when there is no compliance check for vehicle emission standards. Particulate Matter (PM) is one of the pollutants emitted from fuel burning in internal combustion engines and remains suspended in the atmosphere, causing respiratory and cardiovascular health problems to the population. In this study, we analyzed the interaction between vehicular emissions, meteorological variables, and particulate matter concentrations in the lower atmosphere, presenting methods for predicting and forecasting PM2.5.

Methods

Meteorological and vehicle flow data from the city of Curitiba, Brazil, and particulate matter concentration data from optical sensors installed in the city between 2020 and 2022 were organized in hourly and daily averages. Prediction and forecasting were based on two machine learning models: Random Forest (RF) and Long Short-Term Memory (LSTM) neural network. The baseline model for prediction was chosen as the Multiple Linear Regression (MLR) model, and for forecast, we used the naive estimation as baseline.

Results

RF showed that on hourly and daily prediction scales, the planetary boundary layer height was the most important variable, followed by wind gust and wind velocity in hourly or daily cases, respectively. The highest PM prediction accuracy (99.37%) was found using the RF model on a daily scale. For forecasting, the highest accuracy was 99.71% using the LSTM model for 1-h forecast horizon with 5 h of previous data used as input variables.

Discussion

The RF and LSTM models were able to improve prediction and forecasting compared with MLR and Naive, respectively. The LSTM was trained with data corresponding to the period of the COVID-19 pandemic (2020 and 2021) and was able to forecast the concentration of PM2.5 in 2022, in which the data show that there was greater circulation of vehicles and higher peaks in the concentration of PM2.5. Our results can help the physical understanding of factors influencing pollutant dispersion from vehicle emissions at the lower atmosphere in urban environment. This study supports the formulation of new government policies to mitigate the impact of vehicle emissions in large cities.

Mapping nationwide concentrations of sulfate and nitrate in ambient PM2.5 in South Korea using machine learning with ground observation data

Particulate matter (PM) is a major air pollutant in Northeast Asia, with frequent high PM episodes. To investigate the nationwide spatial distribution maps of PM2.5 and secondary inorganic aerosols in South Korea, prediction models for mapping SO42- and NO3- concentrations in PM2.5 were developed using machine learning with ground-based observation data. Specifically, the random forest algorithm was used in this study to predict the SO42- and NO3- concentrations at 548 air quality monitoring stations located within the representative radii of eight intensive air quality monitoring stations. The average concentrations of PM2.5, SO42-, and NO3- across the entire nation were 17.2 ± 2.8, 3.0 ± 0.6, and 3.4 ± 1.2 μg/m3, respectively. The spatial distributions of SO42- and NO3- concentrations in 2021 revealed elevated concentrations in both the western and central regions of South Korea. This result suggests that SO42- concentrations were primarily influenced by industrial activities rather than vehicle emissions, whereas NO3- concentrations were more associated with vehicle emissions. During a high PM2.5 event (November 19-21, 2021), the concentration of SO42- was primarily influenced by SOX emissions from China, while the concentration of NO3- was affected by NOX emissions from both China and Korea. The methodology developed in this study can be used to explore the chemical characteristics of PM2.5 with high spatiotemporal resolution. It can also provide valuable insights for the nationwide mitigation of secondary PM2.5 pollution.

Indoor sulfur dioxide prediction through air quality modeling and assessment of sulfur dioxide and nitrogen dioxide levels in industrial and non-industrial areas

In this study, the levels of sulfur dioxide (SO2) and nitrogen dioxide (NO2) were measured indoors and outdoors using passive samplers in Tymar village (20 homes), an industrial area, and Haji Wsu (15 homes), a non-industrial region, in the summer and the winter seasons. In comparison to Haji Wsu village, the results showed that Tymar village had higher and more significant mean SO2 and NO2 concentrations indoors and outdoors throughout both the summer and winter seasons. The mean outdoor concentration of SO2 was the highest in summer, while the mean indoor NO2 concentration was the highest in winter in both areas. The ratio of NO2 indoors to outdoors was larger than one throughout the winter at both sites. Additionally, the performance of machine learning (ML) approaches: multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) were compared in predicting indoor SO2 concentrations in both the industrial and non-industrial areas. Factor analysis (FA) was conducted on different indoor and outdoor meteorological and air quality parameters, and the resulting factors were employed as inputs to train the models. Cross-validation was applied to ensure reliable and robust model evaluation. RF showed the best predictive ability in the prediction of indoor SO2 for the training set (RMSE = 2.108, MAE = 1.780, and R2 = 0.956) and for the unseen test set (RMSE = 4.469, MAE = 3.728, and R2 = 0.779) values compared to other studied models. As a result, it was observed that the RF model could successfully approach the nonlinear relationship between indoor SO2 and input parameters and provide valuable insights to reduce exposure to this harmful pollutant.

Urban environment and health: a cross-sectional multiregional project based on population health surveys in Spain (DAS-EP project) - study protocol

INTRODUCTION

The European Environment Agency estimates that 75% of the European population lives in cities. Despite the many advantages of city life, the risks and challenges to health arising from urbanisation need to be addressed in order to tackle the growing burden of disease and health inequalities in cities. This study, Urban environment and health: a cross-sectional multiregional project based on population health surveys in Spain (DAS-EP project), aims to investigate the complex association between the urban environmental exposures (UrbEEs) and health.

METHODS AND ANALYSIS

DAS-EP is a Spanish multiregional cross-sectional project that combines population health surveys (PHS) and geographical information systems (GIS) allowing to collect rich individual-level data from 17 000 adult citizens participating in the PHS conducted in the autonomous regions of the Basque Country, Andalusia, and the Valencian Community, and the city of Barcelona in the years 2021-2023. This study focuses on the population living in cities or metropolitan areas with more than 100 000 inhabitants. UrbEEs are described by objective estimates at participants' home addresses by GIS, and subjective indicators present in PHS. The health outcomes included in the PHS and selected for this study are self-perceived health (general and mental), prevalence of chronic mental disorders, health-related quality of life, consumption of medication for common mental disorders and sleep quality. We aim to further understand the direct and indirect effects between UrbEEs and health, as well as to estimate the impact at the population level, taking respondents' sociodemographic and socioeconomic characteristics, and lifestyle into consideration.

ETHICS AND DISSEMINATION

The study was approved by the regional Research Ethics Committee of the Basque Country (Ethics Committee for Research Involving Medicinal Products in the Basque Country; PI2022138), Andalusia (Biomedical Research Ethics Committee of the Province of Granada; 2078-N-22), Barcelona (CEIC-PSMar; 2022/10667) and the Valencian Community (Ethics Committee for Clinical Research of the Directorate General of Public Health and Center for Advanced Research in Public Health; 20221125/04). The results will be communicated to the general population, health professionals, and institutions through conferences, reports and scientific articles.

Monthly average air pollution models using geographically weighted regression in Europe from 2000 to 2019

Detailed spatial models of monthly air pollution levels at a very fine spatial resolution (25 m) can help facilitate studies to explore critical time-windows of exposure at intermediate term. Seasonal changes in air pollution may affect both levels and spatial patterns of air pollution across Europe. We built Europe-wide land-use regression (LUR) models to estimate monthly concentrations of regulated air pollutants (NO2, O3, PM10 and PM2.5) between 2000 and 2019. Monthly average concentrations were collected from routine monitoring stations. Including both monthly-fixed and -varying spatial variables, we used supervised linear regression (SLR) to select predictors and geographically weighted regression (GWR) to estimate spatially-varying regression coefficients for each month. Model performance was assessed with 5-fold cross-validation (CV). We also compared the performance of the monthly LUR models with monthly adjusted concentrations. Results revealed significant monthly variations in both estimates and model structure, particularly for O3, PM10, and PM2.5. The 5-fold CV showed generally good performance of the monthly GWR models across months and years (5-fold CV R2: 0.31-0.66 for NO2, 0.4-0.79 for O3, 0.4-0.78 for PM10, 0.46-0.87 for PM2.5). Monthly GWR models slightly outperformed monthly-adjusted models. Correlations between monthly GWR model were generally moderate to high (Pearson correlation >0.6). In conclusion, we are the first to develop robust monthly LUR models for air pollution in Europe. These monthly LUR models, at a 25 m spatial resolution, enhance epidemiologists to better characterize Europe-wide intermediate-term health effects related to air pollution, facilitating investigations into critical exposure time windows in birth cohort studies.

Short-Term Exposure to Fine Particulate Matter and Nitrogen Dioxide and Mortality in 4 Countries

Importance

The association between short-term exposure to air pollution and mortality has been widely documented worldwide; however, few studies have applied causal modeling approaches to account for unmeasured confounders that vary across time and space.

Objective

To estimate the association between short-term changes in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations and changes in daily all-cause mortality rates using a causal modeling approach.

Design, Setting, and Participants

This cross-sectional study used air pollution and mortality data from Jiangsu, China; California; central-southern Italy; and Germany with interactive fixed-effects models to control for both measured and unmeasured spatiotemporal confounders. A total of 8 963 352 deaths in these 4 regions from January 1, 2015, to December 31, 2019, were included in the study. Data were analyzed from June 1, 2021, to October 30, 2023.

Exposure

Day-to-day changes in county- or municipality-level mean PM2.5 and NO2 concentrations.

Main Outcomes and Measures

Day-to-day changes in county- or municipality-level all-cause mortality rates.

Results

Among the 8 963 352 deaths in the 4 study regions, a 10-μg/m3 increase in daily PM2.5 concentration was associated with an increase in daily all-cause deaths per 100 000 people of 0.01 (95% CI, 0.001-0.01) in Jiangsu, 0.03 (95% CI, 0.004-0.05) in California, 0.10 (95% CI, 0.07-0.14) in central-southern Italy, and 0.04 (95% CI, 0.02- 0.05) in Germany. The corresponding increases in mortality rates for a 10-μg/m3 increase in NO2 concentration were 0.04 (95% CI, 0.03-0.05) in Jiangsu, 0.03 (95% CI, 0.01-0.04) in California, 0.10 (95% CI, 0.05-0.15) in central-southern Italy, and 0.05 (95% CI, 0.04-0.06) in Germany. Significant effect modifications by age were observed in all regions, by sex in Germany (eg, 0.05 [95% CI, 0.03-0.06] for females in the single-pollutant model of PM2.5), and by urbanicity in Jiangsu (0.07 [95% CI, 0.04-0.10] for rural counties in the 2-pollutant model of NO2).

Conclusions and Relevance

The findings of this cross-sectional study contribute to the growing body of evidence that increases in short-term exposures to PM2.5 and NO2 may be associated with increases in all-cause mortality rates. The interactive fixed-effects model, which controls for unmeasured spatial and temporal confounders, including unmeasured time-varying confounders in different spatial units, can be used to estimate associations between changes in short-term exposure to air pollution and changes in health outcomes.

Relation of prenatal and postnatal PM2.5 exposure with cognitive and motor function among preschool-aged children

The literature informing susceptible periods of exposure on children's neurodevelopment is limited. We evaluated the impacts of pre- and postnatal fine particulate matter (PM2.5) exposure on children's cognitive and motor function among 1303 mother-child pairs in the Spanish INMA (Environment and Childhood) Study. Random forest models with temporal back extrapolation were used to estimate daily residential PM2.5 exposures that we averaged across 1-week lags during the prenatal period and 4-week lags during the postnatal period. The McCarthy Scales of Children's Abilities (MSCA) were administered around 5 years to assess general cognitive index (GCI) and several subscales (verbal, perceptual-performance, memory, fine motor, gross motor). We applied distributed lag nonlinear models within the Bayesian hierarchical framework to explore periods of susceptibility to PM2.5 on each MSCA outcome. Effect estimates were calculated per 5 μg/m3 increase in PM2.5 and aggregated across adjacent statistically significant lags using cumulative β (βcum) and 95% Credible Intervals (95%CrI). We evaluated interactions between PM2.5 with fetal growth and child sex. We did not observe associations of PM2.5 exposure with lower GCI scores. We found a period of susceptibility to PM2.5 on fine motor scores in gestational weeks 1-9 (βcum = -2.55, 95%CrI = -3.53,-1.56) and on gross motor scores in weeks 7-17 (βcum = -2.27,95%CrI = -3.43,-1.11) though the individual lags for the latter were only borderline statistically significant. Exposure in gestational week 17 was weakly associated with verbal scores (βcum = -0.17, 95%CrI = -0.26,-0.09). In the postnatal period (from age 0.5-1.2 years), we observed a window of susceptibility to PM2.5 on lower perceptual-performance (β = -2.42, 95%CrI = -3.37,-1.46). Unexpected protective associations were observed for several outcomes with exposures in the later postnatal period. We observed no evidence of differences in susceptible periods by fetal growth or child sex. Preschool-aged children's motor function may be particularly susceptible to PM2.5 exposures experienced in utero whereas the first year of life was identified as a period of susceptibility to PM2.5 for children's perceptual-performance.

Application of machine learning in atmospheric pollution research: A state-of-art review

Machine learning (ML) is an artificial intelligence technology that has been used in atmospheric pollution research due to their powerful fitting ability. In this review, 105 articles related to ML and the atmospheric pollution research are critically reviewed. Applications of ML in the prediction of atmospheric pollution (mainly particulate matters) are systematically described, including the principle of prediction, influencing factors and improvement measures. Researchers can improve the accuracy of the prediction model through three main aspects, namely considering the geographical features of the study area into the model, introducing the physical characteristics of pollutants, matching and optimizing ML models. And by using interpretable ML tools, researchers are able to understand the mechanism of the model and gain in-depth information. Then, the state-of-art applications of ML in the source apportionment of atmospheric particulate matter and the effect of atmospheric pollutants on human health are also described. In addition, the advantages and disadvantages of the current applications of ML in atmospheric pollution research are summarized, and the application perspective of ML in this field is elucidated. Given the scarcity of source apportionment applications and human health research, standardized research methods and specialized ML methods are required in atmospheric pollution research to connect these two disciplines.

Air pollution and oxidative stress in adults suffering from airway diseases. Insights from the Gene Environment Interactions in Respiratory Diseases (GEIRD) multi-case control study

Air pollution is a leading risk factor for global mortality and morbidity. Oxidative stress is a key mechanism underlying air-pollution-mediated health effects, especially in the pathogenesis/exacerbation of airway impairments. However, evidence lacks on subgroups at higher risk of developing more severe outcomes in response to air pollution. This multi-centre study aims to evaluate the association between air pollution and oxidative stress in healthy adults and in patients affected by airway diseases from the Italian GEIRD (Gene Environment Interactions in Respiratory Diseases) multi-case control study. Overall, 1841 adults (49 % females, 20-83 years) were included from four Italian centres: Pavia, Sassari, Turin, and Verona. Following a 2-stage screening process, we identified 1273 cases of asthma, chronic bronchitis, rhinitis, or COPD and 568 controls. Systemic oxidative stress was quantified by urinary 8-isoprostane and 8-OH-dG. Individual residential exposures to NO2, PM10, PM2.5, and O3 were derived using an innovative five-stage machine-learning-based approach. Linear mixed regression models tested the association between oxidative stress biomarkers and air pollution tertiles, adjusting by age, sex, BMI, smoking, education and season, with recruiting centres as random intercept. Only cases exhibited higher levels of log-transformed 8-isoprostane and 8-OH-dG in association with NO2 (β: 0.30 95 % CI: 0.08-0.52 and 0.20 95 % CI: 0.03-0.37), PM10 (0.34 95 % CI: 0.12-0.55 and 0.21 95 % CI: 0.05-0.37) and PM2.5 (0.27 95 % CI: 0.09-0.49 and 0.18 95 % CI: 0.02-0.34) as compared to the first tertile of exposure. No significant associations were observed for summer O3. Our findings suggest that exposure to air pollution may increase systemic oxidative stress levels in people suffering from airway diseases. This introduces a potential novel approach available for future epidemiological studies and Public Health for effective prevention strategies oriented at the quantification of early biological effects in susceptible people, whose additional risk level might be currently underrated. Air-pollution-mediated exacerbations, driven by oxidative stress, still deserve our attention.

A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.

Improved daily estimates of relative humidity at high resolution across Germany: A random forest approach

The lack of readily available methods for estimating high-resolution near-surface relative humidity (RH) and the incapability of weather stations to fully capture the spatiotemporal variability can lead to exposure misclassification in studies of environmental epidemiology. We therefore aimed to predict German-wide 1 × 1 km daily mean RH during 2000-2021. RH observations, longitude and latitude, modelled air temperature, precipitation and wind speed as well as remote sensing information on topographic elevation, vegetation, and the true color band composite were incorporated in a Random Forest (RF) model, in addition to date for capturing the temporal variations of the response-explanatory variables relationship. The model achieved high accuracy (R2 = 0.83) and low errors (Root Mean Square Error (RMSE) of 5.07%, Mean Absolute Percentage Error (MAPE) of 5.19% and Mean Percentage Error (MPE) of - 0.53%), calculated via ten-fold cross-validation. A comparison of our RH predictions with measurements from a dense monitoring network in the city of Augsburg, South Germany confirmed the good performance (R2 ≥ 0.86, RMSE ≤ 5.45%, MAPE ≤ 5.59%, MPE ≤ 3.11%). The model displayed high German-wide RH (22y-average of 79.00%) and high spatial variability across the country, exceeding 12% on yearly averages. Our findings indicate that the proposed RF model is suitable for estimating RH for a whole country in high-resolution and provide a reliable RH dataset for epidemiological analyses and other environmental research purposes.

A spatiotemporal XGBoost model for PM2.5 concentration prediction and its application in Shanghai

This paper innovatively constructed an analytical and forecasting framework to predict PM2.5 concentration levels for 16 municipal districts in Shanghai. By means of XGBoost parameters adjustment, empirical mode decomposition, and model fusion, improvements are made on XGBoost prediction accuracy and stability so that prediction deviation at extreme points can be avoided. The main findings of this paper can be summarized as follows: 1) Compared with the original model, the goodness of fit of the modified XGBoost model on the test set increased by 17 %, and the root mean square error decreased by 28 %; 2) The variation of PM2.5 concentration in Shanghai has a significant seasonal (cyclical) effect, and its fluctuation period is 3 months (a quarter). In winter, the frequency of extreme value points is significantly higher than that in other seasons; 3) In terms of spatial distribution, the PM2.5 concentration in the central city of Shanghai is higher than that in the rural areas, and the PM2.5 concentration gradually decreases from center city to the surrounding areas. The innovation and contribution of this paper can be summarized as follows: 1) EEMD algorithm verified by SSA was used to decompose the original model without reconstructing all subsequences and get the best weighing among each part of the hybrid model by using variable weight assignment; 2) The city was cut into pieces according to administrative districts in avoid of the duplicate analysis when utilizing advised Kriging interpolation; 3) IDW method was applied to verified Kriging interpolation to increase the accuracy; 4) The latitude and longitude were innovatively converted into the arc length of the corresponding spherical surface; 5) Hierarchical analysis method was used to obtain the order of importance among the PM2.5 monitoring stations, which could improve the accuracy and achieve dimension reduction.

Estimating daily air temperature and pollution in Catalonia: A comprehensive spatiotemporal modelling of multiple exposures

Environmental epidemiology studies require models of multiple exposures to adjust for co-exposure and explore interactions. We estimated spatiotemporal exposure to surface air temperature and pollution (PM2.5, PM10, NO2, O3) at high spatiotemporal resolution (daily, 250 m) for 2018-2020 in Catalonia. Innovations include the use of TROPOMI products, a data split for remote sensing gap-filling evaluation, estimation of prediction uncertainty, and use of explainable machine learning. We compiled meteorological and air quality station measurements, climate and atmospheric composition reanalyses, remote sensing products, and other spatiotemporal data. We performed gap-filling of remotely-sensed products using Random Forest (RF) models and validated them using Out-Of-Bag (OOB) samples and a structured data split. The exposure modelling workflow consisted of: 1) PM2.5 station imputation with PM10 data; 2) quantile RF (QRF) model fitting; and 3) geostatistical residual spatial interpolation. Prediction uncertainty was estimated using QRF. SHAP values were used to examine variable importance and the fitted relationships. Model performance was assessed via nested CV at the station level. Evaluation of the gap-filling models using the structured split showed error underestimation when using OOB. Temperature models had the best performance (R2 =0.98) followed by the gaseous air pollutants (R2 =0.81 for NO2 and 0.86 for O3), while the performance of the PM2.5 and PM10 models was lower (R2 =0.57 and 0.63 respectively). Predicted exposure patterns captured urban heat island effects, dust advection events, and NO2 hotspots. SHAP values estimated a high importance of TROPOMI tropospheric NO2 columns in PM and NO2 models, and confirmed that the fitted associations conformed to prior knowledge. Our work highlights the importance of correctly validating gap-filling models and the potential of TROPOMI measurements. Moderate performance in PM models can be partly explained by the poor station coverage. Our exposure estimates can be used in epidemiological studies potentially accounting for exposure uncertainty.

Generating High Spatial Resolution Exposure Estimates from Sparse Regulatory Monitoring Data

Random Forest algorithms have extensively been used to estimate ambient air pollutant concentrations. However, the accuracy of model-predicted estimates can suffer from extrapolation problems associated with limited measurement data to train the machine learning algorithms. In this study, we developed and evaluated two approaches, incorporating low-cost sensor data, that enhanced the extrapolating ability of random-forest models in areas with sparse monitoring data. Rochester, NY is the area of a pregnancy-cohort study. Daily PM2.5 concentrations from the NAMS/SLAMS sites were obtained and used as the response variable in the model, with satellite data, meteorological, and land-use variables included as predictors. To improve the base random-forest models, we used PM2.5 measurements from a pre-existing low-cost sensors network, and then conducted a two-step backward selection to gradually eliminate variables with potential emission heterogeneity from the base models. We then introduced the regression-enhanced random forest method into the model development. Finally, contemporaneous urinary 1-hydroxypyrene was used to evaluate the PM2.5 predictions generated from the two approaches. The two-step approach increased the average external validation R2 from 0.49 to 0.65, and decreased the RMSE from 3.56 μg/m3 to 2.96 μg/m3. For the regression-enhanced random forest models, the average R2 of the external validation was 0.54, and the RMSE was 3.40 μg/m3. We also observed significant and comparable relationships between urinary 1-hydroxypyrene levels and PM2.5 predictions from both improved models. This PM2.5 model estimation strategy could improve the extrapolating ability of random forest models in areas with sparse monitoring data.

Application of machine learning (individual vs stacking) models on MERRA-2 data to predict surface PM2.5 concentrations over India

The spatial coverage of PM2.5 monitoring is non-uniform across India due to the limited number of ground monitoring stations. Alternatively, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), is an atmospheric reanalysis data used for estimating PM2.5. MERRA-2 does not explicitly measure PM2.5 but rather follows an empirical model. MERRA-2 data were spatiotemporally collocated with ground observation for validation across India. Significant underestimation in MERRA-2 prediction of PM2.5 was observed over many monitoring stations ranging from -20 to 60 μg m-3. The utility of Machine Learning (ML) models to overcome this challenge was assessed. MERRA-2 aerosol and meteorological parameters were the input features used to train and test the individual ML models and compare them with the stacking technique. Initially, with 10% of randomly selected data, individual model performance was assessed to identify the best model. XGBoost (XGB) was the best model (r2 = 0.73) compared to Random Forest (RF) and LightGBM (LGBM). Stacking was then applied by keeping XGB as a meta-regressor. Stacked model results (r2 = 0.77) outperformed the best standalone estimate of XGB. Stacking technique was used to predict hourly and daily PM2.5 in different regions across India and each monitoring station. The eastern region exhibited the best hourly prediction (r2 = 0.80) and substantial reduction in Mean Bias (MB = -0.03 μg m-3), followed by the northern region (r2 = 0.63 and MB = -0.10 μg m-3), which showed better output due to the frequent observation of PM2.5 >100 μg m-3. Due to sparse data availability to train the ML models, the lowest performance was for the central region (r2 = 0.46 and MB = -0.60 μg m-3). Overall, India's PM2.5 prediction was good on an hourly basis compared to a daily basis using the ML stacking technique.

Meteorological data source comparison-a case study in geospatial modeling of potential environmental exposure to abandoned uranium mine sites in the Navajo Nation

Meteorological (MET) data is a crucial input for environmental exposure models. While modeling exposure potential using geospatial technology is a common practice, existing studies infrequently evaluate the impact of input MET data on the level of uncertainty on output results. The objective of this study is to determine the effect of various MET data sources on the potential exposure susceptibility predictions. Three sources of wind data are compared: The North American Regional Reanalysis (NARR) database, meteorological aerodrome reports (METARs) from regional airports, and data from local MET weather stations. These data sources are used as inputs into a machine learning (ML) driven GIS Multi-Criteria Decision Analysis (GIS-MCDA) geospatial model to predict potential exposure to abandoned uranium mine sites in the Navajo Nation. Results indicate significant variations in results derived from different wind data sources. After validating the results from each source using the National Uranium Resource Evaluation (NURE) database in a geographically weighted regression (GWR), METARs data combined with the local MET weather station data showed the highest accuracy, with an average R2 of 0.74. We conclude that local direct measurement-based data (METARs and MET data) produce a more accurate prediction than the other sources evaluated in the study. This study has the potential to inform future data collection methods, leading to more accurate predictions and better-informed policy decisions surrounding environmental exposure susceptibility and risk assessment.

Predicting fine-scale daily NO2 over Mexico City using an ensemble modeling approach

In recent years, there has been growing interest in developing air pollution prediction models to reduce exposure measurement error in epidemiologic studies. However, efforts for localized, fine-scale prediction models have been predominantly focused in the United States and Europe. Furthermore, the availability of new satellite instruments such as the TROPOsopheric Monitoring Instrument (TROPOMI) provides novel opportunities for modeling efforts. We estimated daily ground-level nitrogen dioxide (NO2) concentrations in the Mexico City Metropolitan Area at 1-km2 grids from 2005 to 2019 using a four-stage approach. In stage 1 (imputation stage), we imputed missing satellite NO2 column measurements from the Ozone Monitoring Instrument (OMI) and TROPOMI using the random forest (RF) approach. In stage 2 (calibration stage), we calibrated the association of column NO2 to ground-level NO2 using ground monitors and meteorological features using RF and extreme gradient boosting (XGBoost) models. In stage 3 (prediction stage), we predicted the stage 2 model over each 1-km2 grid in our study area, then ensembled the results using a generalized additive model (GAM). In stage 4 (residual stage), we used XGBoost to model the local component at the 200-m2 scale. The cross-validated R2 of the RF and XGBoost models in stage 2 were 0.75 and 0.86 respectively, and 0.87 for the ensembled GAM. Cross-validated rootmean-squared error (RMSE) of the GAM was 3.95 μg/m3. Using novel approaches and newly available remote sensing data, our multi-stage model presented high cross-validated fits and reconstructs fine-scale NO2 estimates for further epidemiologic studies in Mexico City.

Spatiotemporally continuous estimates of daily 1-km PM2.5 concentrations and their long-term exposure in China from 2000 to 2020

Monitoring long-term variations in fine particulate matter (PM_2.5) is essential for environmental management and epidemiological studies. While satellite-based statistical/machine-learning methods can be used for estimating high-resolution ground-level PM_2.5 concentration data, their applications have been hindered by limited accuracy in daily estimates during years without PM_2.5 measurements and massive missing values due to satellite retrieval data. To address these issues, we developed a new spatiotemporal high-resolution PM_2.5 hindcast modeling framework to generate the full-coverage, daily, 1-km PM_2.5 data for China for the period 2000-2020 with improved accuracy. Our modeling framework incorporated information on changes in observation variables between periods with and without monitoring data and filled gaps in PM_2.5 estimates induced by satellite data using imputed high-resolution aerosol data. Compared to previous hindcast studies, our method achieved superior overall cross-validation (CV) R² and root-mean-square error (RMSE) of 0.90 and 12.94 μg/m³ and significantly improved the model performance in years without PM_2.5 measurements, raising the leave-one-year-out CV R² [RMSE] to 0.83 [12.10 μg/m³] at a monthly scale (0.65 [23.29 μg/m³] at a daily scale). Our long-term PM_2.5 estimates show a sharp decline in PM_2.5 exposure in recent years, but the national exposure level in 2020 still exceeded the first annual interim target of the 2021 World Health Organization air quality guidelines. The proposed hindcast framework represents a new strategy to improve air quality hindcast modeling and can be applied to other regions with limited air quality monitoring periods. These high-quality estimates can support both long- and short-term scientific research and environmental management of PM_2.5 in China.

72-hour real-time forecasting of ambient PM2.5 by hybrid graph deep neural network with aggregated neighborhood spatiotemporal information

The observation-based air pollution forecasting method has high computational efficiency over traditional numerical models, but a poor ability in long-term (after 6 h) forecasting due to a lack of detailed representation of atmospheric processes associated with the pollution transport. To address such limitation, here we propose a novel real-time air pollution forecasting model that applies a hybrid graph deep neural network (GNN_LSTM) to dynamically capture the spatiotemporal correlations among neighborhood monitoring sites to better represent the physical mechanism of pollutant transport across the space with the graph structure which is established with features (angle, wind speed, and wind direction) of neighborhood sites to quantify their interactions. Such design substantially improves the model performance in 72-hour PM_2.5 forecasting over the whole Beijing-Tianjin-Hebei region (overall R² increases from 0.6 to 0.79), particularly for polluted episodes (PM_2.5 concentration > 55 µg/m³) with pronounced regional transport to be captured by GNN_LSTM model. The inclusion of the AOD feature further enhances the model performance in predicting PM_2.5 over the sites where the AOD can inform additional aloft PM_2.5 pollution features related to regional transport. The importance of neighborhood site (particularly for those in the upwind flow pathway of the target area) features for long-term PM_2.5 forecast is demonstrated by the increased performance in predicting PM_2.5 in the target city (Beijing) with the inclusion of additional 128 neighborhood sites. Moreover, the newly developed GNN_LSTM model also implies the "source"-receptor relationship, as impacts from distanced sites associated with regional transport grow along with the forecasting time (from 0% to 38% in 72 h) following the wind flow. Such results suggest the great potential of GNN_LSTM in long-term air quality forecasting and air pollution prevention.

Developing PM2.5 and PM10 prediction models on a national and regional scale using open-source remote sensing data

Clean air is the precursor to a healthy life. Air quality is an issue that has been getting under its well-deserved spotlight in the last few years. From a remote sensing point of view, the first Copernicus mission with the main purpose of monitoring the atmosphere and tracking air pollutants, the Sentinel-5P TROPOMI mission, has been widely used worldwide. Particulate matter of a diameter smaller than 2.5 and 10 μm (PM_2.5 and PM₁₀) significantly determines air quality. Still, there are no available satellite sensors that allow us to track them remotely with high accuracy, but only using ground stations. This research aims to estimate PM_2.5 and PM₁₀ using Sentinel-5P and other open-source remote sensing data available on the Google Earth Engine (GEE) platform for heating (December 2021, January, and February 2022) and non-heating seasons (June, July, and August 2021) on the territory of the Republic of Croatia. Ground stations of the National Network for Continuous Air Quality Monitoring were used as a starting point and as ground truth data. Raw hourly data were matched to remote sensing data, and seasonal models were trained at the national and regional scale using machine learning. The proposed approach uses a random forest algorithm with a percentage split of 70% and gives moderate to high accuracy regarding the temporal frame of the data. The mapping gives us visual insight between the ground and remote sensing data and shows the seasonal variations of PM_2.5 and PM₁₀. The results showed that the proposed approach and models could efficiently estimate air quality.

Relationship of long-term air pollution exposure with asthma and rhinitis in Italy: an innovative multipollutant approach

BACKGROUND

air pollution is a complex mixture; novel multipollutant approaches could help understanding the health effects of multiple concomitant exposures to air pollutants.

AIM

to assess the relationship of long-term air pollution exposure with the prevalence of respiratory/allergic symptoms and diseases in an Italian multicenter study using single and multipollutant approaches.

METHODS

14420 adults living in 6 Italian cities (Ancona, Pavia, Pisa, Sassari, Turin, Verona) were investigated in 2005-2011 within 11 different study cohorts. Questionnaire information about risk factors and health outcomes was collected. Machine learning derived mean annual concentrations of PM₁₀, PM_2.5, NO₂ and mean summer concentrations of O₃ (μg/m³) at residential level (1-km resolution) were used for the period 2013-2015. The associations between the four pollutants and respiratory/allergic symptoms/diseases were assessed using two approaches: a) logistic regression models (single-pollutant models), b) principal component logistic regression models (multipollutant models). All the models were adjusted for age, sex, education level, smoking habits, season of interview, climatic index and included a random intercept for cohorts.

RESULTS

the three-year average (± standard deviation) pollutants concentrations at residential level were: 20.3 ± 6.8 μg/m³ for PM_2.5, 29.2 ± 7.0 μg/m³ for PM₁₀, 28.0 ± 11.2 μg/m³ for NO₂, and 70.9 ± 4.3 μg/m³ for summer O_3. Through the multipollutant models the following associations emerged: PM₁₀ and PM_2.5 were related to 14-25% increased odds of rhinitis, 23-34% of asthma and 30-33% of night awakening; NO₂ was related to 6-9% increased odds of rhinitis, 7-8% of asthma and 12% of night awakening; O₃ was associated with 37% increased odds of asthma attacks. Overall, the Odds Ratios estimated through the multipollutant models were attenuated when compared to those of the single-pollutant models.

CONCLUSIONS

this study enabled to obtain new information about the health effects of air pollution on respiratory/allergic outcomes in adults, applying innovative methods for exposure assessment and multipollutant analyses.

Long-term residential exposure to air pollution and risk of chronic respiratory diseases in Italy: The BIGEPI study

Long-term exposure to air pollution has adverse respiratory health effects. We investigated the cross-sectional relationship between residential exposure to air pollutants and the risk of suffering from chronic respiratory diseases in some Italian cities. In the BIGEPI project, we harmonised questionnaire data from two population-based studies conducted in 2007-2014. By combining self-reported diagnoses, symptoms and medication use, we identified cases of rhinitis (n = 965), asthma (n = 328), chronic bronchitis/chronic obstructive pulmonary disease (CB/COPD, n = 469), and controls (n = 2380) belonging to 13 cohorts from 8 Italian cities (Pavia, Turin, Verona, Terni, Pisa, Ancona, Palermo, Sassari). We derived mean residential concentrations of fine particulate matter (PM₁₀, PM_2.5), nitrogen dioxide (NO₂), and summer ozone (O₃) for the period 2013-2015 using spatiotemporal models at a 1 km resolution. We fitted logistic regression models with controls as reference category, a random-intercept for cohort, and adjusting for sex, age, education, BMI, smoking, and climate. Mean ± SD exposures were 28.7 ± 6.0 μg/m³ (PM₁₀), 20.1 ± 5.6 μg/m³ (PM_2.5), 27.2 ± 9.7 μg/m³ (NO₂), and 70.8 ± 4.2 μg/m³ (summer O₃). The concentrations of PM₁₀, PM_2.5, and NO₂ were higher in Northern Italian cities. We found associations between PM exposure and rhinitis (PM₁₀: OR 1.62, 95%CI: 1.19-2.20 and PM_2.5: OR 1.80, 95%CI: 1.16-2.81, per 10 μg/m³) and between NO₂ exposure and CB/COPD (OR 1.22, 95%CI: 1.07-1.38 per 10 μg/m³), whereas asthma was not related to environmental exposures. Results remained consistent using different adjustment sets, including bi-pollutant models, and after excluding subjects who had changed residential address in the last 5 years. We found novel evidence of association between long-term PM exposure and increased risk of rhinitis, the chronic respiratory disease with the highest prevalence in the general population. Exposure to NO₂, a pollutant characterised by strong oxidative properties, seems to affect mainly CB/COPD.

An ensemble mixed spatial model in estimating long-term and diurnal variations of PM2.5 in Taiwan

Meteorology, human activities, and other emission sources drive diurnal cyclic patterns of air pollution. Previous studies mainly focused on the variation of PM_2.5 concentrations during daytime rather than nighttime. In addition, assessing the spatial variations of PM_2.5 in large areas is a critical issue for environmental epidemiological studies to clarify the health effects from PM_2.5 exposures. In terms of air pollution spatial modelling, using only a single model might lose information in capturing spatial and temporal correlation between predictors and pollutant levels. Hence, this study aimed to propose an ensemble mixed spatial model that incorporated Kriging interpolation, land-use regression (LUR), machine learning, and stacking ensemble approach to estimate long-term PM_2.5 variations for nearly three decades in daytime and nighttime. Three steps of model development were applied: 1) linear based LUR and Hybrid Kriging-LUR were used to determine influential predictors; 2) machine learning algorithms were used to enhance model prediction accuracy; 3) predictions from the selected machine learning models were fitted and evaluated again to build the final ensemble mixed spatial model. The results showed that prediction performance increased from 0.514 to 0.895 for daily, 0.478 to 0.879 for daytime, and 0.523 to 0.878 for nighttime when applying the proposed ensemble mixed spatial model compared with LUR. Results of overfitting test and extrapolation ability test confirmed the robustness and reliability of the developed models. The distance to the nearest thermal power plant, density of soil and pebbles fields, and funeral facilities might affect the variation of PM_2.5 levels between daytime and nighttime. The PM_2.5 level was higher in daytime compared with nighttime with little difference, revealing the importance of estimating nighttime PM_2.5 variations. Our findings also clarified the emission sources in daytime and nighttime, which serve as valuable information for air pollution control strategies establishment.

A smart and flexible approach for aggregation of adjacent polygons to meet a minimum target area or attribute value

Many geospatial analyses require flexible aggregation of adjacent units to meet a minimum target area or attribute value. This is usually accomplished using several non-automated and complex GIS tasks. We developed an integrated user-friendly approach and algorithm implemented in the 'GHS-SmartDissolve' tool, which addresses minimum mapping unit or attribute value requirements, layers resolution mismatch, spatial uncertainty or modifiable areal unit problem in GIScience. This method automatically dissolves adjacent features updating fields' values to reach a minimum target area or attribute value, using a flexible settings framework to meet user requirements. Also provided as a toolbox for ArcGIS (Esri), the approach is demonstrated by (i) estimating the mean particulate matter concentrations for all municipalities in Italy in 2011 by combining a coarse grid of global PM2.5 concentrations with fine administrative units; (ii) estimating boundaries of Metropolitan areas in Portugal as aggregation of municipalities, by matching their total population.

Residential exposure to air pollution and adverse respiratory and allergic outcomes in children and adolescents living in a chipboard industrial area of Northern Italy

BACKGROUND

Chipboard production is a source of wood dust, formaldehyde, and combustion-related pollutants such as nitrogen dioxide (NO₂) and particulate matter (PM). In this cohort study, we assessed whether exposures to NO₂, formaldehyde, PM₁₀, PM_2.5, and black carbon were associated with adverse respiratory and allergic outcomes among all 7525 people aged 0-21 years residing in the Viadana district, an area in Northern Italy including the largest chipboard industrial park in the country.

METHODS

Data on hospitalizations, emergency room (ER) admissions, and specialist visits in pneumology, allergology, ophthalmology, and otorhinolaryngology were obtained from the Local Health Unit. Residential air pollution concentrations in 2013 (baseline) were derived using local (Viadana II), national (EPISAT), and continental (ELAPSE) exposure models. Associations were estimated using negative binomial regression models for counts of events occurred during 2013-2017, with follow-up time as an offset term and adjustment for sex, age, nationality, and a census-block socio-economic indicator.

RESULTS

Median annual exposures to NO₂, PM₁₀, and PM_2.5 were below the European Union annual air quality standards (40, 40, and 25 μg/m³) but above the World Health Organization 2021 air quality guideline levels (10, 15, and 5 μg/m³). Exposures to NO₂ and PM_2.5 were significantly associated with higher rates of ER pneumology admissions (13 to 30 % higher rates per interquartile range exposure differences, all p < 0.01). Higher rates of allergology and ophthalmology visits were found for participants exposed to higher pollutants' concentrations. When considering the 4-km buffer around the industries, associations with respiratory hospitalizations became significant, and associations with ER pneumology admissions, allergology and ophthalmology visits became stronger. Formaldehyde was not associated with the outcomes considered.

CONCLUSION

Using administrative indicators of health effects a priori attributable to air pollution, we documented the adverse impact of long-term air pollution exposure in residential areas close to the largest chipboard industries in Italy. These findings, combined with evidence from previous studies, call for an action to improve air quality through preventive measures especially targeting emissions related to the industrial activities.

Scientometric and multidimensional contents analysis of PM2.5 concentration prediction

The foundation for the environmental department to take suitable measures and make a significant contribution towards improving air quality is the precise and dependable prediction of PM_2.5 concentration. It is essential to review the development process and hotspots of PM_2.5 concentration prediction studies over the past 20 years (2000-2021) comprehensively and quantitatively. This study used detailed bibliometric methods and CiteSpace software to visually analyze the PM_2.5 pollution level. The outcomes found that the prediction research phases of PM_2.5 can be broadly divided into three phases and enter the rapid growth phase after 2017. Five categories of keywords are clustered, and the forecasting data and forecasting methods are typical cluster representatives. Then, the construction and processing of PM_2.5 concentration prediction datasets, the prediction methods and technical processes, and the determination of the prediction spatial-temporal scales are the main content of the analysis. In the future, it is necessary to concentrate on multi-source data fusion for PM_2.5 concentration prediction at multiple spatial-temporal scales and focus on technology integration and innovative applications in forecasting models, especially the optimal use of deep machine learning methods to improve prediction accuracy and practical application conversion.

Prediction of effect of wind speed on air pollution level using machine learning technique

Air pollution is one of the most challenging issues poses serious threat to human health and environment. The increasing influx of population in metropolitan cities has further worsened the situation. Quantifying the air pollution experimentally is quite a challenging task as it depends on many parameters viz., wind speed, wind temperature, relative humidity, temperature etc. It requires the investment of huge money and manpower for controlling air pollution. Machine learning technique-based computer modelling reduces both of the parameters. In the present work, the dependence of air pollution level on wind speed and temperature has been taken up using machine learning in the form of ANN and LSTM model. The recorded data of air pollution level (PM2.5) is collected from a measurement station of Lucknow city situated at Central School, CPCB. The data is used in an Artificial Neural based network and in an LSTM model to predict suitably the level of air pollution for a known value of average wind speed and temperature without experimental measurements. LSTM model is found to predict the pollution level better than ANN for the developed ANN networks.

Air Pollution and Birth Outcomes: Health Impact and Economic Value Assessment in Spain

Air pollution is considered an ongoing major public health and environmental issue around the globe, affecting the most vulnerable, such as pregnant women and fetuses. The aim of this study is to estimate the health impact and economic value on birth outcomes, such as low birthweight (LBW), preterm birth (PTB), small for gestational age (SGA), attributable to a reduction of PM₁₀ levels in Spain. Reduction based on four scenarios was implemented: fulfillment of WHO guidelines and EU limits, and an attributable reduction of 15% and 50% in annual PM₁₀ levels. Retrospective study on 288,229 live-born singleton children born between 2009-2010, using data from Spain Birth Registry Statistics database, as well as mean PM₁₀ mass concentrations. Our finding showed that a decrease in annual exposure to PM₁₀ appears to be associated with a decrease in the annual cases of LBW, SGA and PTB, as well as a reduction in hospital cost attributed to been born with LBW. Improving pregnancy outcomes by reducing the number of LBW up to 5% per year, will result in an estimate associated monetary saving of 50,000 to 7,000,000 euros annually. This study agrees with previous literature and highlights the need to implement, and ensure compliance with, stricter policies that regulate the maximum exposure to outdoor PM permitted in Spain, contributing to decreased environmental health risk, especially negative birth outcomes.

High spatiotemporal resolution estimation of AOD from Himawari-8 using an ensemble machine learning gap-filling method

The data incompleteness of aerosol optical depth (AOD) products and their lack of availability in highly urbanized areas limit their great potential of application in air quality research. In this study, we developed an ensemble machine-learning approach that integrated random forest-based Space Interpolation Model (SIM) and deep neural network-based Time Interpolation Model (TIM) to achieve high spatiotemporal resolution dataset of AOD. The spatial interpolation model first filled the spatial gaps in the Level-2 Himawari-8 hourly AOD product in 0.05° (∼5 km) spatial resolution, while the time interpolation model further improved the temporal resolution to 10 min on its basis. A full-coverage AOD dataset of Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) in 2020 was obtained as a practical implementation. The validation against in-situ AOD observations from AERONET and SONET indicated that this new dataset was satisfactory (R = 0.80), and especially in spring and summer. Overall, our ensemble machine-learning model provided an effective scheme for reconstruction of AOD with high spatiotemporal resolution of 0.05° and 10 min, which may further advance the near-real-time monitoring of air-quality in urban areas.

Susceptible windows of exposure to fine particulate matter and fetal growth trajectories in the Spanish INMA (INfancia y Medio Ambiente) birth cohort

While prior studies report associations between fine particulate matter (PM2.5) exposure and fetal growth, few have explored temporally refined susceptible windows of exposure. We included 2328 women from the Spanish INMA Project from 2003 to 2008. Longitudinal growth curves were constructed for each fetus using ultrasounds from 12, 20, and 34 gestational weeks. Z-scores representing growth trajectories of biparietal diameter, femur length, abdominal circumference (AC), and estimated fetal weight (EFW) during early (0-12 weeks), mid- (12-20 weeks), and late (20-34 weeks) pregnancy were calculated. A spatio-temporal random forest model with back-extrapolation provided weekly PM2.5 exposure estimates for each woman during her pregnancy. Distributed lag non-linear models were implemented within the Bayesian hierarchical framework to identify susceptible windows of exposure for each outcome and cumulative effects [βcum, 95% credible interval (CrI)] were aggregated across adjacent weeks. For comparison, general linear models evaluated associations between PM2.5 averaged across multi-week periods (i.e., weeks 1-11, 12-19, and 20-33) and fetal growth, mutually adjusted for exposure during each period. Results are presented as %change in z-scores per 5 μg/m3 in PM2.5, adjusted for covariates. Weeks 1-6 [βcum = -0.77%, 95%CrI (-1.07%, -0.47%)] were identified as a susceptible window of exposure for reduced late pregnancy EFW while weeks 29-33 were positively associated with this outcome [βcum = 0.42%, 95%CrI (0.20%, 0.64%)]. A similar pattern was observed for AC in late pregnancy. In linear regression models, PM2.5 exposure averaged across weeks 1-11 was associated with reduced late pregnancy EFW and AC; but, positive associations between PM2.5 and EFW or AC trajectories in late pregnancy were not observed. PM2.5 exposures during specific weeks may affect fetal growth differentially across pregnancy and such associations may be missed by averaging exposure across multi-week periods, highlighting the importance of temporally refined exposure estimates when studying the associations of air pollution with fetal growth.

A gap-filling hybrid approach for hourly PM2.5 prediction at high spatial resolution from multi-sourced AOD data

Despite a growing interest in the satellite derived estimation of ground-level PM_2.5 concentrations, modeling hourly PM_2.5 levels at high spatial resolution with complete coverage for a large study domain remains a challenge. The primary modeling challenges lie in the presence of missing data in aerosol optical depth (AOD) and the limited data resolution for a single-platformed satellite AOD product. To address these issues, we developed a gap-filling hybrid approach to estimate full coverage hourly ground-level PM_2.5 concentrations at a high spatial resolution of 1 km using multi-platformed and multi-scale satellite derived AOD products. Specifically, we filled the gaps and downscaled the multi-sourced AOD from Geostationary Ocean Color Imager (GOCI), Multi-Angle Implementation of Atmospheric Correction (MAIAC), and Modern-Era Retrospective Analysis for Research and Applications - version 2 (MERRA-2), using a hybrid data fusion approach. The fused hourly AOD with full coverage was then used for hourly PM_2.5 predictions at a high spatial resolution of 1 km. We demonstrated the application of the proposed approach and assessed its performance using the data collected from northeastern Asia from 2015 to 2019. Our fused hourly AOD data showed high accuracy with the mean absolute error of 0.14 and correlation coefficient of 0.94, in validation against Aerosol Robotic Network (AERONET) AOD. Our AOD-based PM_2.5 prediction model showed a good prediction accuracy with cross-validated R² of 0.85 and root mean squared error of 12.40 μg/m³, respectively. Given that the highly resolved PM_2.5 predictions captured both the temporal trend and the peak of PM_2.5 pollution scenarios, we concluded that the proposed hybrid approach can effectively combine multi-sourced satellite AOD and derive subsequent PM_2.5 distributions at high spatial and temporal resolutions.

GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms

Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore, accurate prediction of air quality is crucial for mitigation planning to support urban sustainability and resilience. Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI). Incorporating PM in AQI studies is crucial because of its easily inhalable micro-size which has adverse impacts on ecology, environment, and human health. Accurate and timely prediction of the air quality index can ensure adequate intervention to aid air quality management. Therefore, this study undertakes a spatial hazard assessment of the air quality index using particulate matter with a diameter of 10 μm or lesser (PM10) in Selangor, Malaysia, by developing four machine learning models: eXtreme Gradient Boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), and Naive Bayes (NB). Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70% of the dataset, while 30% was used for cross-validation. Results showed that XGBoost has the highest overall accuracy and precision of 0.989 and 0.995, followed by random forest (0.989, 0.993), K-nearest neighbour (0.987, 0.984), and Naive Bayes (0.917, 0.922), respectively. The spatial air quality maps were generated by integrating the geographical information system (GIS) with the four MLAs, which correlated with Malaysia's air pollution index. The maps indicate that air quality in Selangor is satisfactory and posed no threats to health. Nevertheless, the two algorithms with the best performance (XGBoost and RF) indicate that a high percentage of the air quality is moderate. The study concludes that successful air pollution management policies such as green infrastructure practice, improvement of energy efficiency, and restrictions on heavy-duty vehicles can be adopted in Selangor and other Southeast Asian cities to prevent deterioration of air quality in the future.

Innovative experimental approach for spatial mapping of source-specific risk contributions of potentially toxic trace elements in PM10

Exposure to potentially toxic trace elements (PTTEs) in inhalable particulate matter (PM₁₀) is associated with an increased risk of developing cardiorespiratory diseases. Therefore, in multi-source polluted urban contexts, a spatially-resolved evaluation of health risks associated with exposure to PTTEs in PM is essential to identify critical risk areas. In this study, a very-low volume device for high spatial resolution sampling and analysis of PM₁₀ was employed in Terni (Central Italy) in a wide and dense network (23 sampling sites, about 1 km between each other) during a 15-month monitoring campaign. The soluble and insoluble fraction of 33 elements in PM₁₀ was analysed through a chemical fractionation procedure that increased the selectivity of the elements as source tracers. Total carcinogenic risk (CR) and non-carcinogenic risk (NCR) for adults and children due to concentrations of PTTEs in PM₁₀ were calculated and quantitative source-specific risk apportionment was carried out by applying Positive Matrix Factorization (PMF) to the spatially-resolved concentrations of the chemically fractionated elements. PMF analysis identified 5 factors: steel plant, biomass burning, brake dust, soil dust and road dust. Steel plant showed the greatest risk contribution. Total CR and NCR, and source-specific risk contributions at the 23 sites were interpolated using the ordinary kriging (OK) method and mapped to geo-reference the health risks of the identified sources in the whole study area. This also allowed risk estimation in areas not directly measured and the assessment of the risk contribution of individual sources at each point of the study area. This innovative experimental approach is an effective tool to localize the health risks of spatially disaggregated sources of PTTEs and it may allow for better planning of control strategies and mitigation measures to reduce airborne pollutant concentrations in urban settings polluted by multiple sources.

Ensemble averaging using remote sensing data to model spatiotemporal PM10 concentrations in sparsely monitored South Africa

There is a paucity of air quality data in sub-Saharan African countries to inform science driven air quality management and epidemiological studies. We investigated the use of available remote-sensing aerosol optical depth (AOD) data to develop spatially and temporally resolved models to predict daily particulate matter (PM₁₀) concentrations across four provinces of South Africa (Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape) for the year 2016 in a two-staged approach. In stage 1, a Random Forest (RF) model was used to impute Multiangle Implementation of Atmospheric Correction AOD data for days where it was missing. In stage 2, the machine learner algorithms RF, Gradient Boosting and Support Vector Regression were used to model the relationship between ground-monitored PM₁₀ data, AOD and other spatial and temporal predictors. These were subsequently combined in an ensemble model to predict daily PM₁₀ concentrations at 1 km × 1 km spatial resolution across the four provinces. An out-of-bag R² of 0.96 was achieved for the first stage model. The stage 2 cross-validated (CV) ensemble model captured 0.84 variability in ground-monitored PM₁₀ with a spatial CV R² of 0.48 and temporal CV R² of 0.80. The stage 2 model indicated an optimal performance of the daily predictions when aggregated to monthly and annual means. Our results suggest that a combination of remote sensing data, chemical transport model estimates and other spatiotemporal predictors has the potential to improve air quality exposure data in South Africa's major industrial provinces. In particular, the use of a combined ensemble approach was found to be useful for this area with limited availability of air pollution ground monitoring data.

PM2.5 Concentrations Variability in North China Explored with a Multi-Scale Spatial Random Effect Model

Compiling fine-resolution geospatial PM_2.5 concentrations data is essential for precisely assessing the health risks of PM_2.5 pollution exposure as well as for evaluating environmental policy effectiveness. In most previous studies, global and local spatial heterogeneity of PM_2.5 is captured by the inclusion of multi-scale covariate effects, while the modelling of genuine scale-dependent variabilities pertaining to the spatial random process of PM_2.5 has not yet been much studied. Consequently, this work proposed a multi-scale spatial random effect model (MSSREM), based a recently developed fixed-rank Kriging method, to capture both the scale-dependent variabilities and the spatial dependence effect simultaneously. Furthermore, a small-scale Monte Carlo simulation experiment was conducted to assess the performance of MSSREM against classic geospatial Kriging models. The key results indicated that when the multiple-scale property of local spatial variabilities were exhibited, the MSSREM had greater ability to recover local- or fine-scale variations hidden in a real spatial process. The methodology was applied to the PM_2.5 concentrations modelling in North China, a region with the worst air quality in the country. The MSSREM provided high prediction accuracy, 0.917 R-squared, and 3.777 root mean square error (RMSE). In addition, the spatial correlations in PM_2.5 concentrations were properly captured by the model as indicated by a statistically insignificant Moran's I statistic (a value of 0.136 with p-value > 0.2). Overall, this study offers another spatial statistical model for investigating and predicting PM_2.5 concentration, which would be beneficial for precise health risk assessment of PM_2.5 pollution exposure.

A Spatial-Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction

Accurate and fine-grained prediction of PM_2.5 concentration is of great significance for air quality control and human physical and mental health. Traditional approaches, such as time series, recurrent neural networks (RNNs) or graph convolutional networks (GCNs), cannot effectively integrate spatial-temporal and meteorological factors and manage dynamic edge relationships among scattered monitoring stations. In this paper, a spatial-temporal causal convolution network framework, ST-CCN-PM_2.5, is proposed. Both the spatial effects of multi-source air pollutants and meteorological factors are considered via spatial attention mechanism. Time-dependent features in causal convolution networks are extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-PM_2.5 are tuned by Bayesian optimization. Haikou air monitoring station data are employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results include the following points: (1) For a single station, the RMSE, MAE and R² values of ST-CCN-PM_2.5 decreased by 27.05%, 10.38% and 3.56% on average, respectively. (2) For all stations, ST-CCN-PM_2.5 achieve the best performance in win-tie-loss experiments. The numbers of winning stations are 68, 63, and 64 out of 95 stations in RMSE (MSE), MAE, and R², respectively. In addition, the mean MSE, RMSE and MAE of ST-CCN-PM_2.5 are 4.94, 2.17 and 1.31, respectively, and the R² value is 0.92. (3) Shapley analysis shows wind speed is the most influencing factor in fine-grained PM_2.5 concentration prediction. The effects of CO and temperature on PM_2.5 prediction are moderately significant. Friedman test under different resampling further confirms the advantage of ST-CCN-PM_2.5. The ST-CCN-PM_2.5 provides a promising direction for fine-grained PM_2.5 prediction.

Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia

Rapid economic growth, industrialization, and urbanization have caused frequent air pollution events in East Asia over the last few decades. Recently, aerosol data from geostationary satellite sensors have been used to monitor ground-level particulate matter (PM) concentrations hourly. However, many studies have focused on using historical datasets to develop PM estimation models, often decreasing their predictability for unseen data in new days. To mitigate this problem, this study proposes a novel real-time learning (RTL) approach to estimate PM with aerodynamic diameters of <10 μm (PM₁₀) and <2.5 μm (PM_2.5) using hourly aerosol data from the Geostationary Ocean Color Imager (GOCI) and numerical model outputs for daytime conditions over Northeast Asia. Three schemes with different weighting strategies were evaluated using 10-fold cross-validation (CV). The RTL models, which considered both concentration and time as weighting factors (i.e., Scheme 3) yielded consistent improvement for 10-fold CV performance on both hourly and monthly scales. The real-time calibration results for PM₁₀ and PM_2.5 were R² = 0.97 and 0.96, and relative root mean square error (rRMSE) = 12.1% and 12.0%, respectively, and the 10-fold CV results for PM₁₀ and PM_2.5 were R² = 0.73 and 0.69 and rRMSE = 41.8% and 39.6%, respectively. These results were superior to results from the offline models in previous studies, which were based on historical data on an hourly scale. Moreover, we estimated PM concentrations in the ocean without using land-based variables, and clearly demonstrated the PM transport over time. Because the proposed models are based on the RTL approach, the density of in-situ monitoring sites could be a major uncertainty factor. This study identified that a high error occurred in low-density areas, whereas a low error occurred in high-density areas. The proposed approach can be operated to monitor ground-level PM concentrations in real-time with uncertainty analysis to ensure optimal results.

Spatio-temporal trajectory evolution and cause analysis of air pollution in Chengdu, China

This study comprehensively analyzed air pollution in Chengdu (CD), a megacity in southwest China, evaluated the Variation Characteristics of air quality during 2015-2018, and conducted Random Forest classification of air pollution data of 2017. The classification results showed three pollution periods: severe (December, January and February), ozone (May‒August), and slight (March and November). These features were combined with potential source contribution function (PSCF), concentration weighted trajectory (CWT) and backward trajectory model (HYSPLIT) for simulating spatio-temporal trajectory of air polluted during each pollution periods. The results show that PM_2.5 mainly comes from CD and surrounding cities, and some may be from India, Myanmar and Chongqing; PM₁₀ mainly comes from CD and surrounding cities and some may be from India and Myanmar; NO₂ mainly comes from CD and surrounding cities and cities and Some of the pollution may come from the input of India, Myanmar, Chongqing and Inner Mongolia; O₃ mainly comes from the urban agglomeration of Sichuan Basin and some areas from Chongqing, Sichuan Liangshan and Yunnan Guizhou. Combined with the meteorological data of temperature, relative humidity and wind speed, aerosol optical depth, planetary boundary layer height and thermal anomaly data, the Monthly, daily and hourly spatio-temporal characteristics and the possible occurred cause of the main air pollution during each pollution period in CD were revealed detail. The research in this paper is critical for pollution control and prevention and provides a scientific basis for studying the spatio-temporal characteristics and sources of pollution in megacities in terrain such as basins and mountains.Implications: Air pollution has a significant impact on human and ecological health. In 2013, Chengdu was one of the five cities with the most serious PM_2.5 pollution in the world. In the previous study of air pollution in Chengdu, it was only for a short period of pollution. It is impossible to fully understand the spatio-temporal trajectory and cause of air pollution. Chengdu is surrounded by mountains, and the meteorological conditions have been stagnant for a long time. The research on the spatio-temporal evolution of the main air pollution trajectories in each pollution period in Chengdu is particularly important. Quantifying the pollution trajectory and air pollution concentration is helpful to fully understand the air quality in Chengdu. The comprehensive analysis of multi-source data such as air pollution and meteorology has focused on strengthening the in-depth research on the transmission law of air pollution, the spatio-temporal change trend of air pollution, the sources of air pollution and the causes of air pollution, so as to help people fully understand the sources and causes of pollution in Chengdu. Aiming at the trajectory law, causes and occurrence time of air pollution, it is conducive for the government to formulate corresponding policies, carry out regional emission reduction and joint prevention and control, improve air quality and minimize the harm of air pollution to the public.

Effect of fire spread, flame characteristic, fire intensity on particulate matter 2.5 released from surface fuel combustion of Pinus koraiensis plantation- A laboratory simulation study

PM2.5 is one of major pollutants emitted from forest fires. High PM2.5 concentration not only affects short-term human respiration health, but also poses a long-term threat to human cardiopulmonary functionality. Therefore, it is of great importance to quantitatively assess the PM2.5 released by forest combustion in forest fire studies. In this study we examine relationships between the PM2.5 concentration and environment and fuel characteristics laboratory experiments. In the experiments, fuel beds with controlled moisture contents and loads were first built; then 144 ignition experiments were conducted for various combinations of wind speeds using a wind tunnel device. Fire behavior characteristics and PM2.5 concentrations released from fuel combustion were measured and analyzed. The experimental results show that the relationship between fire characteristics, fire intensity and the influencing factors of wind speed, fuel moisture content, and fuel load can be explained by the fundamental theory of forest combustion. Although PM2.5 concentration rises with the increase of wind speed, the decrease of fuel moisture content, and the increase of fuel load, there appears to be a fuel load threshold for a given combination of wind speed and fuel moisture content that the increase of PM2.5 concentration decelerates quickly after the load passes the threshold value. After screening fire behavior characteristics that affect PM2.5 concentration, we found that fire line intensity and flame width are the ones with the strongest association with the concentration. With flame width as independent variable, we have built two regression models to predict PM2.5 and fire line intensity which are treated as dependent variable; the models have high accuracy with R2 = 0.92 for predicting PM2.5 and R2 = 0.97 for predicting fire line intensity. Study results can be used as reference to protect the health of forest fire fighters, and can be helpful for forest fire smoke management.

Full-Coverage PM2.5 Mapping and Variation Assessment during the Three-Year Blue-Sky Action Plan Based on a Daily Adaptive Modeling Approach

Owing to a series of air pollution prevention and control policies, China’s PM2.5 pollution has greatly improved; however, the long-term spatial contiguous products that facilitate the analysis of the distribution and variation of PM2.5 pollution are insufficient. Due to the limitations of missing values in aerosol optical depth (AOD) products, the reconstruction of full-coverage PM2.5 concentration remains challenging. In this study, we present a two-stage daily adaptive modeling framework, based on machine learning, to solve this problem. We built the annual models in the first stage, then daily models were constructed in the second stage based on the output of the annual models, which incorporated the parameter and feature adaptive tuning strategy. Within this study, PM2.5 concentrations were adaptively modeled and reconstructed daily based on the multi-angle implementation of atmospheric correction (MAIAC) AOD products and other ancillary data, such as meteorological factors, population, and elevation. Our model validation showed excellent performance with an overall R2 = 0.91 and RMSE = 9.91 μg/m3 for the daily models, along with the site-based cross-validation R2s and RMSEs of 0.86–0.87 and 12–12.33 μg/m3; these results indicated the reliability and feasibility of the proposed approach. The daily full-coverage PM2.5 concentrations at 1 km resolution across China during the Three-Year Blue-Sky Action Plan were reconstructed in this study. We analyzed the distribution and variations of reconstructed PM2.5 at three different time scales. Overall, national PM2.5 pollution has significantly improved with the annual average concentration dropping from 33.67–28.03 μg/m3, which demonstrated that air pollution control policies are effective and beneficial. However, some areas still have severe PM2.5 pollution problems that cannot be ignored. In conclusion, the approach proposed in this study can accurately present daily full-coverage PM2.5 concentrations and the research outcomes could provide a reference for subsequent air pollution prevention and control decision-making.

Random Forest Estimation and Trend Analysis of PM2.5 Concentration over the Huaihai Economic Zone, China (2000–2020)

Consisting of ten cities in four Chinese provinces, the Huaihai Economic Zone has suffered serious air pollution over the last two decades, particularly of fine particulate matter (PM2.5). In this study, we used multi-source data, namely MAIAC AOD (at a 1 km spatial resolution), meteorological, topographic, date, and location (latitude and longitude) data, to construct a regression model using random forest to estimate the daily PM2.5 concentration over the Huaihai Economic Zone from 2000 to 2020. It was found that the variable expressing time (date) had the greatest characteristic importance when estimating PM2.5. By averaging the modeled daily PM2.5 concentration, we produced a yearly PM2.5 concentration dataset, at a 1 km resolution, for the study area from 2000 to 2020. On comparing modeled daily PM2.5 with observational data, the coefficient of determination (R2) of the modeling was 0.85, the root means square error (RMSE) was 14.63 μg/m3, and the mean absolute error (MAE) was 10.03 μg/m3. The quality assessment of the synthesized yearly PM2.5 concentration dataset shows that R2 = 0.77, RMSE = 6.92 μg/m3, and MAE = 5.42 μg/m3. Despite different trends from 2000–2010 and from 2010–2020, the trend of PM2.5 concentration over the Huaihai Economic Zone during the 21 years was, overall, decreasing. The area of the significantly decreasing trend was small and mainly concentrated in the lake areas of the Zone. It is concluded that PM2.5 can be well-estimated from the MAIAC AOD dataset, when incorporating spatiotemporal variability using random forest, and that the resultant PM2.5 concentration data provide a basis for environmental monitoring over large geographic areas.

Functional Kriging for Spatiotemporal Modeling of Nitrogen Dioxide in a Middle Eastern Megacity

Long-term hour-specific air pollution exposure estimates have rarely been of interest in epidemiological research. However, this can be relevant for studies that aim to estimate the residential exposure for the hours that subjects mostly spend time there, or for those hours that they may work in another location. Here, we developed a model by spatially predicting the long-term diurnal curves of nitrogen dioxide (NO2) in Tehran, Iran, one of the most polluted and populated megacities in the Middle East. We used the statistical framework of functional data analysis (FDA) including ordinary kriging for functional data (OKFD) and functional analysis of variance (fANOVA) for modeling. The long-term NO2 diurnal curves had two distinct maxima and minima. The absolute minimum value of the city average was 40.6 ppb (around 4:00 p.m.) and the absolute maximum value was 52.0 ppb (around 10:00 p.m.). The OKFD showed the concentrations, the diurnal maximum/minimum values, and their corresponding occurring times varied across the city. The fANOVA highlighted that the effect of population density on the NO2 concentrations is not constant and depends on time within the diurnal period. The provided estimation of long-term hour-specific maps can inform future epidemiological studies to use the long-term mean for specific hour(s) of the day. Moreover, the demonstrated FDA framework can be used as a set of flexible statistical methods.

High-Resolution Urban Air Quality Mapping for Multiple Pollutants Based on Dense Monitoring Data and Machine Learning

Spatially explicit urban air quality information is important for urban fine-management and public life. However, existing air quality measurement methods still have some limitations on spatial coverage and system stability. A micro station is an emerging monitoring system with multiple sensors, which can be deployed to provide dense air quality monitoring data. Here, we proposed a method for urban air quality mapping at high-resolution for multiple pollutants. By using the dense air quality monitoring data from 448 micro stations in Lanzhou city, we developed a decision tree model to infer the distribution of citywide air quality at a 500 m × 500 m × 1 h resolution, with a coefficient of determination (R²) value of 0.740 for PM_2.5, 0.754 for CO and 0.716 for SO₂. Meanwhile, we also show that the deployment density of the monitoring stations can have a significant impact on the air quality inference results. Our method is able to show both short-term and long-term distribution of multiple important pollutants in the city, which demonstrates the potential and feasibility of dense monitoring data combined with advanced data science methods to support urban atmospheric environment fine-management, policy making, and public health studies.

Development and Evaluation of Statistical Models Based on Machine Learning Techniques for Estimating Particulate Matter (PM2.5 and PM10) Concentrations

Despite extensive research on air pollution estimation/prediction, inter-country models for estimating air pollutant concentrations in Southeast Asia have not yet been fully developed and validated owing to the lack of air quality (AQ), emission inventory and meteorological data from different countries in the region. The purpose of this study is to develop and evaluate two machine learning (ML)-based models (i.e., analysis of covariance (ANCOVA) and random forest regression (RFR)) for estimating daily PM_2.5 and PM₁₀ concentrations in Brunei Darussalam. These models were first derived from past AQ and meteorological measurements in Singapore and then tested with AQ and meteorological data from Brunei Darussalam. The results show that the ANCOVA model (R2 = 0.94 and RMSE = 0.05 µg/m³ for PM_2.5, and R2 = 0.72 and RMSE = 0.09 µg/m³ for PM₁₀) could describe daily PM concentrations over 18 µg/m³ in Brunei Darussalam much better than the RFR model (R2 = 0.92 and RMSE = 0.04 µg/m³ for PM_2.5, and R2 = 0.86 and RMSE = 0.08 µg/m³ for PM₁₀). In conclusion, the derived models provide a satisfactory estimation of PM concentrations for both countries despite some limitations. This study shows the potential of the models for inter-country PM estimations in Southeast Asia.

Environmental Pollution Analysis and Impact Study-A Case Study for the Salton Sea in California

A natural experiment conducted on the shrinking Salton Sea, a saline lake in California, showed that each one foot drop in lake elevation resulted in a 2.6% average increase in PM2.5 concentrations. The shrinking has caused the asthma rate continues to increase among children, with one in five children being sent to the emergency department, which is related to asthma. In this paper, several data-driven machine learning (ML) models are developed for forecasting air quality and dust emission to study, evaluate and predict the impacts on human health due to the shrinkage of the sea, such as the Salton Sea. The paper presents an improved long short-term memory (LSTM) model to predict the hourly air quality (O3 and CO) based on air pollutants and weather data in the previous 5 h. According to our experiment results, the model generates a very good R2 score of 0.924 and 0.835 for O3 and CO, respectively. In addition, the paper proposes an ensemble model based on random forest (RF) and gradient boosting (GBoost) algorithms for forecasting hourly PM2.5 and PM10 using the air quality and weather data in the previous 5 h. Furthermore, the paper shares our research results for PM2.5 and PM10 prediction based on the proposed ensemble ML models using satellite remote sensing data. Daily PM2.5 and PM10 concentration maps in 2018 are created to display the regional air pollution density and severity. Finally, the paper reports Artificial Intelligence (AI) based research findings of measuring air pollution impact on asthma prevalence rate of local residents in the Salton Sea region. A stacked ensemble model based on support vector regression (SVR), elastic net regression (ENR), RF and GBoost is developed for asthma prediction with a good R2 score of 0.978.