Air Pollutant Concentrations Research Articles

An application of low-cost sensors to monitor children's exposure to air pollution at five schools in Queensland, Australia

There are local variations in the concentration of air pollutants that may not be captured by the existing monitoring networks; however, they can be important because this is where community exposures occur. Using low-cost monitors (LCM) located at relevant community locations, such as schools, can provide information on local sources, exposure and, with the participation of the community, can help to mitigate these problems. As such, this study was conducted to assess the outdoor air quality (PM2.5 and CO) at five schools in Queensland, Australia, over a period of one year using KOALA LCMs. The results allowed us to broadly conclude on the significance of local sources, such as to show that the majority of PM2.5 in the outdoor school environments did not originate from motor vehicle emissions, being more likely from other background sources such as bushfire smoke and surface dust. By comparing the data obtained by two KOALAs, one at the roadside and the other well away from roads, we estimated that only 37% of the PM2.5 near the road originated from motor vehicles. This contrasts with the known observation that, near busy roads, over 80% of particles by number originate from vehicle emissions. This study provides a model for further investigation and helps to improve the understanding of the importance of air quality among schoolchildren and the local communities.

Impact of single and combined local air pollution mitigation measures in an urban environment

Urban air pollution is one of the most important environmental problems for human health and several strategies have been developed for its mitigation. The objective of this study is to assess the impact of single and combined mitigation measures on concentrations of air pollutants emitted by traffic at pedestrian level in the same urban environment. The effectiveness of different scenarios of green infrastructure (GI), the implementation of photocatalytic materials and traffic low emission zones (LEZ) are investigated, as well as several combinations of LEZ and GI. A wide set of scenarios is simulated through Computational Fluid Dynamics (CFD) modelling for two different wind directions (perpendicular (0°) and 45° wind directions). Wind flow for the BASE scenario without any measure implemented was previously evaluated using wind-tunnel measurements. Air pollutant concentrations for this scenario are compared with the results obtained from the different mitigation scenarios. Reduction of traffic emissions through LEZ is found to be the most effective single measure to improve local air quality. However, GI enhances the effects of LEZ, which makes the combination of LEZ + GI a very effective measure. The effectiveness of this combination depends on the GI layout, the intensity of emission reduction in the LEZ and the traffic diversion in streets surrounding the LEZ. These findings, in line with previous literature, suggest that the implementation of GI may increase air pollutant concentrations at pedestrian level for some cases. However, this study highlights that this negative effect on air quality can turn into positive when used in combination with reductions of local traffic emissions.

Indoor Air Sources of Outdoor Air Pollution: Health Consequences, Policy, and Recommendations: An Official American Thoracic Society Workshop Report.

Indoor sources of air pollution worsen indoor and outdoor air quality. Thus, identifying and reducing indoor pollutant sources would decrease both indoor and outdoor air pollution, benefit public health, and help address the climate crisis. As outdoor sources come under regulatory control, unregulated indoor sources become a rising percentage of the problem. This American Thoracic Society workshop was convened in 2022 to evaluate this increasing proportion of indoor contributions to outdoor air quality. The workshop was conducted by physicians and scientists, including atmospheric and aerosol scientists, environmental engineers, toxicologists, epidemiologists, regulatory policy experts, and pediatric and adult pulmonologists. Presentations and discussion sessions were centered on 1) the generation and migration of pollutants from indoors to outdoors, 2) the sources and circumstances representing the greatest threat, and 3) effective remedies to reduce the health burden of indoor sources of air pollution. The scope of the workshop was residential and commercial sources of indoor air pollution in the United States. Topics included wood burning, natural gas, cooking, evaporative volatile organic compounds, source apportionment, and regulatory policy. The workshop concluded that indoor sources of air pollution are significant contributors to outdoor air quality and that source control and filtration are the most effective measures to reduce indoor contributions to outdoor air. Interventions should prioritize environmental justice: Households of lower socioeconomic status have higher concentrations of indoor air pollutants from both indoor and outdoor sources. We identify research priorities, potential health benefits, and mitigation actions to consider (e.g., switching from natural gas to electric stoves and transitioning to scent-free consumer products). The workshop committee emphasizes the benefits of combustion-free homes and businesses and recommends economic, legislative, and education strategies aimed at achieving this goal.

Impact of air pollution: tourists’ decision making behaviour during rural tourism

In recent years, the increasingly severe air pollution has not only posed a threat to residents’ health but also had a significant influence on tourists’ travel decision making behaviours. This study utilised empirical dynamic model–long short-term memory model to predict air pollutant concentrations in Xidi Village, China, and investigated their impact on tourists’ travel decisions. According to the results, there is a negative correlation between air quality and air quality index (AQI). The lowest AQIs were recorded in May and August 2022, with visitor numbers reaching 12 305 and 11 705, respectively. During the period from May to October, except for ozone (O3), which reached a maximum concentration of 157 μg/m3, all other pollutant concentrations remained at low levels. According to the predictions of the model, Xidi Village often experiences high concentrations of air pollutants during the spring and winter seasons, leading to hazy weather. This information provides accurate air quality data for tourists, helping them avoid periods of severe pollution at their travel destinations and reducing travel risks.

"Predictive Modelling of Air Quality Index (AQI) Across Diverse Cities and States of India using Machine Learning: Investigating the Influence of Punjab's Stubble Burning on AQI Variability"

Air pollution is a common and serious problem nowadays and it cannot be ignored as it has harmful impacts on human health. To address this issue proactively, people should be aware of their surroundings, which means the environment where they survive. With this motive, this research has predicted the AQI based on different air pollutant concentrations in the atmosphere. The dataset used for this research has been taken from the official website of CPCB. The dataset has the air pollutant concentration from 22 different monitoring stations in different cities of Delhi, Haryana, and Punjab. This data is checked for null values and outliers. But, the most important thing to note is the correct understanding and imputation of such values rather than ignoring or doing wrong imputation. The time series data has been used in this research which is tested for stationarity using The Dickey-Fuller test. Further different ML models like CatBoost, XGBoost, Random Forest, SVM regressor, time series model SARIMAX, and deep learning model LSTM have been used to predict AQI. For the performance evaluation of different models, I used MSE, RMSE, MAE, and R2. It is observed that Random Forest performed better as compared to other models.

Non-thermal plasma mitigation of low concentration of air pollutants: removal of isopropyl alcohol using transition metal-oxide integration.

The present work studied the decomposition of isopropyl alcohol (IPA), widely used in chemical industries and households, in a packed-bed dielectric barrier discharge (DBD) plasma reactor. Metal oxide (MOx) coated on γ-Al2O3 (M = Cu, Mn, Co) was utilized for packing. The plasma-packed mode was a likely alternative to the conventional removal techniques, as it aids the conversion of dilute concentrations of IPA to CO and CO2 at ambient conditions (room temperature and atmospheric pressure). The mean electron energy calculations suggest that electrons with higher energy are generated when the discharge zone is packed with catalysts. When comparing IPA conversion (input concentration of 25ppm) for no packing mode and MOx/γ-Al2O3 coupled plasma mode, the latter method enhances conversion to greater than 90% at an applied voltage of 18kV. Also, MOx/γ-Al2O3 showed the highest selectivity to CO2 (70%) compared to plasma-only mode (45%). The metal-oxide layer provides the necessary catalytic surface facilitating the oxidation of IPA to COx through active oxygen species or the interaction of surface hydroxyl groups. The use of MOx/γ-Al2O3 resulted in about 90% carbon balance and reduced ozone generation, demonstrating the significance of integrating metal oxide to achieve efficient conversion and maximal selectivity towards the desired products.

Inequalities in urban air pollution in sub-Saharan Africa: an empirical modeling of ambient NO and NO2 concentrations in Accra, Ghana.

Road traffic has become the leading source of air pollution in fast-growing sub-Saharan African cities. Yet, there is a dearth of robust city-wide data for understanding space-time variations and inequalities in combustion related emissions and exposures. We combined nitrogen dioxide (NO2) and nitric oxide (NO) measurement data from 134 locations in the Greater Accra Metropolitan Area (GAMA), with geographical, meteorological, and population factors in spatio-temporal mixed effects models to predict NO2 and NO concentrations at fine spatial (50 m) and temporal (weekly) resolution over the entire GAMA. Model performance was evaluated with 10-fold cross-validation (CV), and predictions were summarized as annual and seasonal (dusty [Harmattan] and rainy [non-Harmattan]) mean concentrations. The predictions were used to examine population distributions of, and socioeconomic inequalities in, exposure at the census enumeration area (EA) level. The models explained 88% and 79% of the spatiotemporal variability in NO2 and NO concentrations, respectively. The mean predicted annual, non-Harmattan and Harmattan NO2 levels were 37 (range: 1-189), 28 (range: 1-170) and 50 (range: 1-195) µg m-3, respectively. Unlike NO2, NO concentrations were highest in the non-Harmattan season (41 [range: 31-521] µg m-3). Road traffic was the dominant factor for both pollutants, but NO2 had higher spatial heterogeneity than NO. For both pollutants, the levels were substantially higher in the city core, where the entire population (100%) was exposed to annual NO2 levels exceeding the World Health Organization (WHO) guideline of 10 µg m-3. Significant disparities in NO2 concentrations existed across socioeconomic gradients, with residents in the poorest communities exposed to levels about 15 µg m-3 higher compared with the wealthiest (p < 0.001). The results showed the important role of road traffic emissions in air pollution concentrations in the GAMA, which has major implications for the health of the city's poorest residents. These data could support climate and health impact assessments as well as policy evaluations in the city.

Analysis of Primary Air Pollutants’ Spatiotemporal Distributions Based on Satellite Imagery and Machine-Learning Techniques

Accurate monitoring of air pollution is crucial to human health and the global environment. In this research, the various multispectral satellite data, including MODIS AOD/SR, Landsat 8 OLI, and Sentinel-2, together with the two most commonly used machine-learning models, viz. multi-layer backpropagation neural network (MLBPN) and random forest (RF), have been employed to analyze the spatiotemporal distributions of the primary air pollutant from 2019 to 2022 in Guanzhong Region, China. In the conducted experiments, the RF-based model, using the MODIS AOD data, has generally demonstrated the “optimal” estimation performance for the ground-surface concentrations of the primary air-pollutants. Then, the “optimal” estimation model has been employed to analyze the spatiotemporal distribution of the various air pollutants—in terms of temporal distribution, the annual average concentrations of PM2.5, PM10, NO2, and SO2 in the research area showed a decreasing trend from 2019 to 2022, while the annual average concentration of CO remained relatively stable and the annual average concentration of O3 slightly increased; in terms of the spatial distribution, the air pollution presents a gradual increase from west to east in the research area, with the distribution of higher concentrations in the center of the built-up areas and lower in the surrounding rural areas. The proposed estimation model and spatiotemporal analysis can provide reliable methodologies and data support for the further study of the air pollution characteristics in the research area.

High-resolution dispersion modelling of PM2.5, PM10, NOx and NO2 exposure in metropolitan areas in Sweden 2000‒2018 – large health gains due to decreased population exposure

Ambient air pollution remains the major environmental cause of disease. Accurate assessment of population exposure and small-scale spatial exposure variations over long time periods is essential for epidemiological studies. We estimated annual exposure to fine and coarse particulate matter (PM2.5, PM10), and nitrogen oxides (NOx, NO2) with high spatial resolution to examine time trends 2000‒2018, compliance with the WHO Air Quality Guidelines, and assess the health impact. The modelling area covered six metropolitan areas in Sweden with a combined population of 5.5 million. Long-range transported air pollutants were modelled using a chemical transport model with bias correction, and locally emitted air pollutants using source-specific Gaussian-type dispersion models at resolutions up to 50 × 50 m. The modelled concentrations were validated using quality-controlled monitoring data. Lastly, we estimated the reduction in mortality associated with the decrease in population exposure. The validity of modelled air pollutant concentrations was good (R2 for PM2.5 0.84, PM10 0.61, and NOx 0.87). Air pollution exposure decreased substantially, from a population weighted mean exposure to PM2.5 of 12.2 µg m−3 in 2000 to 5.4 µg m−3 in 2018. We estimated that the decreased exposure was associated with a reduction of 2719 (95% CI 2046–3055) premature deaths annually. However, in 2018, 65%, 8%, and 42% of residents in the modelled areas were still exposed to PM2.5, PM10, or NO2 levels, respectively, that exceeded the current WHO Air Quality Guidelines for annual average exposure. This emphasises the potential public health benefits of reductions in air pollution emissions.

Impact of comprehensive air pollution control policies on six criteria air pollutants and acute myocardial infarction morbidity, Weifang, China: A quasi-experimental study

Comprehensive air pollution control policies may reduce pollutant emissions. However, the impact on disease morbidity of the change for the concentration of air pollutants following the policies has been insufficiently studied. We aim to assess the impact of comprehensive air pollution control policies on the levels of six criteria air pollutants and acute myocardial infarction (AMI) morbidity in Weifang, China. This study performed an interrupted time series analysis. The linear model with spline terms and generalized additive quasi-Poisson model were used to estimate the immediate change from 2016 to 2019 in the daily concentration of six air pollutants (PM2.5, PM10, SO2, NO2, O3, and, CO) and AMI incident cases (Age ≥35) associated with the implementation of air pollution control policies in Weifang, respectively. After the implementation of air pollution control policies, air quality in Weifang had been improved. Specifically, the daily concentrations of PM2.5, PM10, SO2, and, CO immediately decreased by 27.9 % (95 % CI: 6.6 % to 44.3 %), 32.9 % (95 % CI: 17.5 % to 45.5 %), 14.6 % (95 % CI: 0.4 % to 26.8 %), and 33.9 % (95 % CI: 22.0 % to 44.0 %), respectively. In addition, the policies implementation was also associate with the immediate decline in the AMI morbidity (−6.5 %, 95 % CI: −10.4 % to −2.3 %). And subgroup analyses indicate that the health effects of the policy intervention were only observed in female (−9.4 %, 95 % CI: −14.4 % to −4.2 %) and those aged ≥65 years (−10.5 %, 95 % CI: −14.6 % to −6.2 %). During the final 20 months of the study period, the policy intervention was estimated to prevent 1603 (95 % CI: 574 to 2587) cases of incident AMI in Weifang. Our results provide strong rationale that the policy intervention significantly reduced ambient pollutant concentrations and AMI morbidity, which highlighted the importance for a comprehensive and rigorous air pollution control policy in regions with severe air pollution.

Associations of Indoor Ventilation Frequency with Depression and Anxiety in Chinese Older Adults

Depression and anxiety carry an important public health burden. Indoor air pollution is associated with depression and anxiety. Ventilation can reduce the concentration of indoor air pollution and improve indoor air quality. This study explored the relationship between indoor ventilation frequency and depression and anxiety in older adults using the data from the 2018 Chinese Longitudinal Healthy Longevity Survey. Compared with older people with low indoor ventilation frequency, those with high indoor ventilation frequency had 51% lower odds of depression (OR=0.49, 95% CI: 0.43 to 0.57) and 37% lower odds of anxiety (OR=0.63, 95% CI: 0.43 to 0.91), and those with intermediate indoor ventilation frequency had 35% lower odds of depression (OR=0.65, 95% CI: 0.56 to 0.75) and 45% lower odds of anxiety (OR=0.55, 95% CI: 0.37 to 0.82). The results were similar across the seasons. However, there were sex, age, lifestyle, and cooking fuel use-specific differences in these associations. The findings emphasize that high ventilation frequency may be conducive to improving mental health in older adults, especially women, the old elder, nonsmokers, nondrinkers, and those who do not exercise and cooked at home.

Combined effect of changes in NO2, O3, PM2.5, SO2 and CO concentrations on small airway dysfunction.

When multiple complex air pollutants are combined in real-world settings, the reliability of estimating the effect of a single pollutant is questionable. This study aimed to investigate the combined effects of changes in air pollutants on small airway dysfunction (SAD). We analysed Korea National Health and Nutrition Examination Survey (KNHANES) V-VIII database from 2010 to 2018 to elucidate the associations between annual changes in air pollutants over a previous 5-year period and small airway function. We estimated the annual concentrations of five air pollutants: NO2, O3, PM2.5, SO2 and CO. Forced expiratory flow between 25% and 75% of vital capacity (FEF25%-75%) <65% was defined as SAD. Using the quantile generalized-Computation (g-Computation) model, the combined effect of the annual changes in different air pollutants was estimated. A total of 29,115 individuals were included. We found significant associations between SAD and the quartiles of annual changes in NO2 (OR = 1.10, 95% CI = 1.08-1.12), O3 (OR = 1.03, 95% CI = 1.00-1.05), PM2.5 (OR = 1.03, 95% CI = 1.00-1.05), SO2 (OR = 1.04, 95% CI = 1.02-1.08) and CO (OR = 1.16, 95% CI = 1.12-1.19). The combined effect of the air pollutant changes was significantly associated with SAD independent of smoking (OR = 1.31, 95% CI = 1.26-1.35, p-value <0.001), and this trend was consistently observed across the entire study population and various subgroup populations. As the estimated risk of SAD, determined by individual-specific combined effect models, increased and the log odds for SAD increased linearly. The combined effect of annual changes in multiple air pollutant concentrations were associated with an increased risk of SAD.

Differential response of various pollutant-meteorology factors on O3 in key regions of China: Based on multiple methods and datasets

Scientific control is urgently required to address O3 pollution in key regions of China. Therefore, based on air pollutant concentrations and meteorological datasets, the methods including distributed lag nonlinear models, Kolmogorov–Zurbenko filter and stepwise multiple linear regression (SMLR) were applied to analyze and clarify the response of pollution and meteorological factors to O3, as well as the comprehensive impact under different time dimensions in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Chengdu-Chongqing regions (CY). Results indicate O3 concentrations in the study area fluctuated at high pollution levels. The effects of various air pollutants and meteorological factors on O3 varied among cities. Temperature is the dominant driver for O3, followed by solar radiation and relative humidity. The influence of wind vector indicates different transport sources of O3 and its precursors. Direct photochemical process based on SMLR fitting coefficient calculation is the main pathway for O3 generation, with contribution of 68.7%–93.8%. In terms of air-pollutant factors, U-shaped response between PM2.5 and O3 in all cities highlighted the interaction of the two air pollutants. The undeniable impact of NO2 on O3 is reflected in its range of effect. Meteorological adjustments led to −10.4–15.1 μg/m3 variations on O3 during 2014–2021, but performed obvious spatiotemporal differences. The changes in anthropogenic emissions deteriorated O3 pollution from 2014 to 2017, while have fluctuated effect since 2018. From the actual cases in special periods, in order to alleviate O3 pollution, it is necessary to strengthen the understanding of the synergistic contribution of meteorology and anthropogenic emissions.

Assessment of the industrial emissions impact on the quality of atmospheric air in the Burlin district of West Kazakhstan region DEVELOPMENT

The article is devoted to the study of air pollution in the Burlin district of the West Kazakhstan region, where one of the main sources of air pollution is the Karachagan oil and gas condensate field, the main feature of which is the high content of hydrogen sulfide in natural gas from 4 to 4.3%. The analysis showed that the atmospheric air state in the specified area is determined by the characteristics of harmful emissions sources, composition, volumes of pollutant emissions and climatic characteristics of the territory, which determine the conditions for the polluting components dispersion. The qualitative and quantitative characteristics of the chemical composition of the atmospheric air in the study area, as well as the features of their changes over the years end climatic seasons, are largely determined by emissions of nitrogen oxides, carbon, sulfur dioxide, hydrogen sulfide, volatile organic compounds and inorganic dust into the atmosphere by oil and gas facilities. The anthropogenic influence of industry in this area is also evidenced by the calculated value of the integral coefficient of air pollution. At the same time, industrial enterprises, having increased the anthropogenic impact on the environment, have the ability to prevent negative environmental consequences. The system of industrial environmental control of the chemical composition of industrial emissions at the Karachagnak field facilities is carried out on an ongoing basis. The analysis showed that a reduction in emissions and, accordingly, a reduction in ground-level concentrations of air pollutants at the Karachagnak field facilities is ensured by a whole range of technological and organizational measures, among which the technology of high-pressure gas reinjection deserves special attention. It necessitates the need to carry out systematic work to study the ecological state of the territory of the Karachagnak field, using not only ground-based research methods, but also modern geoinformation and aerospace technologies that make it possible to dynamically assess the changes taking place, conduct a geo-ecological assessment and make a forecast for the development of the environmental situation in this area. territories. The work carried out contributes to further analysis of the regional assessment of the environmental state of the atmosphere of large industrial centers.

Estimating annual ambient air pollution using structural properties of road networks

In recent years, the world has become increasingly concerned about air pollution. Particularly, High-Income Countries (HIC) and Upper Middle-Income Countries (UMIC) are implementing systems to monitor air pollution on a large scale to aid decision-making. Such efforts are essential, but they have at least three shortcomings: (1) they are costly; (2) they are slow to deploy; and (3) they focus on urban areas, which leads to urban-rural inequalities. Here, we show that we can estimate annual air pollution using open-source information about the structural properties of roads; we focus on England and Wales in the United Kingdom (UK) in this paper, although we argue that our methods are independent of specific country features. Our approach is an inexpensive method of estimating annual air pollution concentrations to an accuracy level that can underpin policymakers’ decisions while providing an estimate in all districts, not just urban areas. Furthermore, we contend that our process is interpretable and explainable.

A261 INVESTIGATING THE RELATIONSHIP BETWEEN AIR POLLUTION AND BIOMARKERS OF CROHN’S DISEASE IN CANADA

Abstract Background The cause of Crohn's disease (CD) remains unclear. Studies suggest a potential connection between CD and air pollution, with a higher occurrence of CD found in areas with higher pollution levels. Aims Our study sought to explore this potential connection by investigating the relationship between components of air pollution and biomarkers indicative of CD risk. Methods In total, 2,256 healthy first-degree relatives of patients with CD were recruited as part of the CCC-GEM Project. We assessed baseline samples of gut permeability using the urinary fractional excretion ratio of lactulose-to-mannitol (LMR), subclinical inflammation via fecal calprotectin (FCP), and stool microbiome using 16S-rRNA sequencing. Air pollutant (n=8) concentrations were obtained from the National Air Pollution Surveillance Database. We used postal code to estimate air pollutant exposure via inverse distance weighting, averaging data for one (pollutant1-month), three (pollutant3-months), and twelve months (pollutant12-months) prior to recruitment. We used generalized estimating equation models to find associations between pollutant exposure and LMR, FCP, and microbiome composition, adjusting for age, sex, air pollutant seasonality, familial income, and familial relationships. Significance was set at pampersand:003C0.05, and false discovery rate (q) correction was applied to microbiome analysis. Results We observed a negative association between particulate matter (PMµg/m3), PM101-month, and PM103-months and LMR (β=-0.06[-0.09, -0.01], p=6.0×10-3 and β=-0.05[-0.07, -0.01], p=7.6×10-4), and a positive association between PM103-months and FCP (β=0.05[0.001, 0.10], p=0.04). 12-month pollutant exposure was associated with seven microbial taxa in a logit model. Notably, Carbon monoxide (CO)12-months was associated with Oscillospiraceae UCG_003 (β=3.49[1.53, 5.46], q=0.03). PM2512-months was associated with Marvinbryantia (β=0.12[0.06, 0.18], q=0.02). PM1012-months was associated with Moryella (β=0.07[0.03, 0.10], q=0.02). Nitrogen dioxide (NO)12-months was associated with Coprobacter and Ruminococcaceae UBA1819 (β=0.05[0.02, 0.07], q=0.03 and β=0.05[0.02, 0.08], q=0.04). NOX12-months was also associated with Ruminococcaceae_UBA1819 as well as Intestinimonas (β=0.03[0.01, 0.04], q=0.04 and β=0.03[0.01, 0.04], q=0.02). Conclusions Air pollutant exposure was associated with biomarkers of gut inflammation, barrier function, and microbiome composition. A shorter exposure (1/3 months) primarily affected FCP and LMR, while longer-term exposure (12 months) exerted a discernible impact on the microbiome. These findings suggest that exposure to air pollution may modulate the gut barrier function, inflammation, and the gut microbiome, biomarkers that are associated with the development of CD. Funding Agencies Crohn’s and Colitis Canada Genetics Environment Microbial (CCC-GEM) III, Leona M. and Harry B. Helmsley Charitable Trust, Dr. Croitoru is the recipient of the Canada Research Chair in Inflammatory Bowel Diseases

A Review of the Interactive Effects of Climate and Air Pollution on Human Health in China.

Through a systematic search of peer-reviewed epidemiologic studies, we reviewed the literature on the human health impacts of climate and ambient air pollution, focusing on recently published studies in China. Selected previous literature is discussed where relevant in tracing the origins. Climate variables and air pollution have a complex interplay in affecting human health. The bulk of the literature we reviewed focuses on the air pollutants ozone and fine particulate matter and temperatures (including hot and cold extremes). The interaction between temperature and ozone presented substantial interaction, but evidence about the interactive effects of temperature with other air pollutants is inconsistent. Most included studies used a time-series design, usually with daily mean temperature and air pollutant concentration as independent variables. Still, more needs to be studied about the co-occurrence of climate and air pollution. The co-occurrence of extreme climate and air pollution events is likely to become an increasing health risk in China and many parts of the world as climate changes. Climate change can interact with air pollution exposure to amplify risks to human health. Challenges and opportunities to assess the combined effect of climate variables and air pollution on human health are discussed in this review. Implications from epidemiological studies for implementing coordinated measures and policies for addressing climate change and air pollution will be critical areas of future work.

Outdoor air pollution exposure and uterine cancer incidence in the sister study.

Outdoor air pollution is a ubiquitous exposure that includes endocrine-disrupting and carcinogenic compounds that may contribute to the risk of hormone-sensitive outcomes such as uterine cancer. However, there is limited evidence about the relationship between outdoor air pollution and uterine cancer incidence. We investigated the associations of residential exposure to particulate matter less than 2.5 µm in diameter (PM2.5) and nitrogen dioxide (NO2) with uterine cancer among 33,417 Sister Study participants with an intact uterus at baseline (2003-2009). Annual average air pollutant concentrations were estimated at participants' geocoded primary residential address(es) using validated spatiotemporal models. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between time-varying 12-month PM2.5 (µg/m3) and NO2 (ppb) averages and uterine cancer incidence. Over a median follow-up period of 9.8 years, 319 incident uterine cancer cases were identified. A 5-ppb increase in NO2 was associated with a 23% higher incidence of uterine cancer (HR = 1.23, 95% CI 1.04-1.46), especially among participants living in urban areas (HR = 1.53, 95% CI: 1.13-2.07). However, PM2.5 was not associated with increased uterine cancer incidence. In this large U.S. cohort, NO2, a marker of vehicular traffic exposure, was associated with a higher incidence of uterine cancer. These findings expand the scope of health effects associated with air pollution, supporting the need for policy and other interventions designed to reduce air pollutant exposure.

Enhancing risk communication and environmental crisis management through satellite imagery and AI for air quality index estimation

Due to climate change, the air pollution problem has become more and more prominent [23]. Air pollution has impacts on people globally, and is considered one of the leading risk factors for premature death worldwide; it was ranked as number 4 according to the website [24]. A study, ‘The Global Burden of Disease,’ reported 4,506,193 deaths were caused by outdoor air pollution in 2019 [22,25]. The air pollution problem is become even more apparent when it comes to developing countries [22], including Thailand, which is considered one of the developing countries [26]. In this research, we focus and analyze the air pollution in Thailand, which has the annual average PM2.5 (particulate matter 2.5) concentration falls in between 15 and 25, classified as the interim target 2 by 2021′s WHO AQG (World Health Organization's Air Quality Guidelines) [27]. (The interim targets refer to areas where the air pollutants concentration is high, with 1 being the highest concentration and decreasing down to 4 [27,28]). However, the methodology proposed here can also be adopted in other areas as well.During the winter in Thailand, Bangkok and its surrounding metroplex have been facing the issue of air pollution (e.g., PM2.5) every year. Currently, air quality measurement is done by simply implementing physical air quality measurement devices at designated—but limited number of locations. In this work, we propose a method that allows us to estimate the Air Quality Index (AQI) on a larger scale by utilizing Landsat 8 images with machine learning techniques. We propose and compare hybrid models with pure regression models to enhance AQI predictionbased on satellite images. Our hybrid model consists of two parts as follows:•The classification part and the estimation part, whereas the pure regressor model consists of only one part, which is a pure regression model for AQI estimation.•The two parts of the hybrid model work hand in hand such that the classification part classifies data points into each class of air quality standard, which is then passed to the estimation part to estimate the final AQI.From our experiments, after considering all factors and comparing their performances, we conclude that the hybrid model has a slightly better performance than the pure regressor model, although both models can achieve a generally minimum R2 (R2 > 0.7). We also introduced and tested an additional factor, DOY (day of year), and incorporated it into our model. Additional experiments with similar approaches are also performed and compared. And, the results also show that our hybrid model outperform them.Keywords: climate change, air pollution, air quality assessment, air quality index, AQI, machine learning, AI, Landsat 8, satellite imagery analysis, environmental data analysis, natural disaster monitoring and management, crisis and disaster management and communication.

Advancing sustainable air quality through calibration of miniature air quality monitors with SRA-SVR combined model

Effective calibration of miniature air quality monitor measurements is an important task to ensure accurate measurements and guarantee sustainable air quality. The aim of this study is to calibrate the measurement data of miniature air quality monitors using Stepwise Regression Analysis and Support Vector Regression (SRA-SVR) combined model. Firstly, a stepwise regression analysis model is used to find a linear relationship between the measured data from the miniature air quality monitor and the air pollutant concentration. Secondly, support vector regression is used to extract the non-linear relationships which affect the pollutant concentrations hidden in the residuals of the stepwise regression analysis model. Finally, the residual calibration values of the SVR model outputs are added to the SRA model outputs to obtain the final outputs of the SRA-SVR combined model for the pollutants. Mean absolute error, relative mean absolute percent error and root mean square error are used to compare the effectiveness of the SRA-SVR combined model and some other commonly used statistical models for the calibration of miniature air quality monitors. The results show that the SRA-SVR combination model performs optimally on both the training and test sets, regardless of which pollutant and which indicator. The SRA-SVR combined model not only has the advantages of the SRA model’s strong interpretability and the SVR model’s high accuracy, but also has higher accuracy than the single model. By using this model to calibrate the measurements of the miniature air quality monitor, its accuracy can be improved by 61.33%–87.43%.