Effects of air pollution in Spatio-temporal modeling of asthma-prone areas using a machine learning model

A study on identifying synergistic prevention and control regions for PM2.5 and O3 and exploring their spatiotemporal dynamic in China

Air pollutants, notably ozone (O3) and fine particulate matter (PM2.5) give rise to evident adverse impacts on public health and the ecotope, prompting extensive global apprehension. Though PM2.5 has been effectively mitigated in China, O3 has been emerging as a primary pollutant, especially in summer. Currently, alleviating PM2.5 and O3 synergistically faces huge challenges. The synergistic prevention and control (SPC) regions of PM2.5 and O3 and their spatiotemporal patterns were still unclear. To address the above issues, this study utilized ground monitoring station data, meteorological data, and auxiliary data to predict the China High-Resolution O3 Dataset (CHROD) via a two-stage model. Furthermore, SPC regions were identified based on a spatial overlay analysis using a Geographic Information System (GIS). The standard deviation ellipse was employed to investigate the spatiotemporal dynamic characteristics of SPC regions. Some outcomes were obtained. The two-stage model significantly improved the accuracy of O3 concentration prediction with acceptable R2 (0.86), and our CHROD presented higher spatiotemporal resolution compared with existing products. SPC regions exhibited significant spatiotemporal variations during the Blue Sky Protection Campaign (BSPC) in China. SPC regions were dominant in spring and autumn, and O3-controlled and PM2.5-dominated zones were detected in summer and winter, respectively. SPC regions were primarily located in the northwest, north, east, and central regions of China, specifically in the Beijing-Tianjin-Hebei urban agglomeration (BTH), Shanxi, Shaanxi, Shandong, Henan, Jiangsu, Xinjiang, and Anhui provinces. The gravity center of SPC regions was distributed in the BTH in winter, and in Xinjiang during spring, summer, and autumn. This study can supply scientific references for the collaborative management of PM2.5 and O3.

A pruned feed-forward neural network (pruned-FNN) approach to measure air pollution exposure

Environmental epidemiology studies require accurate estimations of exposure intensities to air pollution. The process from air pollutant emission to individual exposure is however complex and nonlinear, which poses significant modeling challenges. This study aims to develop an exposure assessment model that can strike a balance between accuracy, complexity, and usability. In this regard, neural networks offer one possible approach. This study employed a custom-designed pruned feed-forward neural network (pruned-FNN) approach to calculate the air pollution exposure index based on emission time and rates, terrain factors, meteorological conditions, and proximity measurements. The model's performance was evaluated by cross-validating the estimated exposure indexes with ground-based monitoring records. The pruned FNN can predict pollution exposure indexes (PEIs) that are highly and stably correlated with the monitored air pollutant concentrations (Spearman's rank correlation coefficients for tenfold cross-validation (mean ± standard deviation: 0.906 ± 0.028) and for random cross-validation (0.913 ± 0.024)). The predicted values are also close to the ground truth in most cases (95.5% of the predicted PEIs have relative errors smaller than 10%) when the training datasets are sufficiently large and well-covered. The pruned-FNN method can make accurate exposure estimations using a flexible number of variables and less extensive data in a less money/time-consuming manner. Compared to other exposure assessment models, the pruned FNN is an appropriate and effective approach for exposure assessment that covers a large geographic area over a long period of time.

EAACI guidelines on environmental science in allergic diseases and asthma - Leveraging artificial intelligence and machine learning to develop a causality model in exposomics

Allergic diseases and asthma are intrinsically linked to the environment we live in and to patterns of exposure. The integrated approach to understanding the effects of exposures on the immune system includes the ongoing collection of large-scale and complex data. This requires sophisticated methods to take full advantage of what this data can offer. Here we discuss the progress and further promise of applying artificial intelligence and machine-learning approaches to help unlock the power of complex environmental data sets toward providing causality models of exposure and intervention. We discuss a range of relevant machine-learning paradigms and models including the way such models are trained and validated together with examples of machine learning applied to allergic disease in the context of specific environmental exposures as well as attempts to tie these environmental data streams to the full representative exposome. We also discuss the promise of artificial intelligence in personalized medicine and the methodological approaches to healthcare with the final AI to improve public health.

Application of genetic algorithm in optimization parallel ensemble-based machine learning algorithms to flood susceptibility mapping using radar satellite imagery

Floods are the natural disaster that occurs most frequently due to the weather and causes the most widespread destruction. The purpose of the proposed research is to analyze flood susceptibility mapping (FSM) in the Sulaymaniyah province of Iraq. This study employed a genetic algorithm (GA) to fine-tune parallel ensemble-based machine learning algorithms (random forest (RF) and bootstrap aggregation (Bagging)). Four machine learning algorithms (RF, Bagging, RF-GA, and Bagging-GA) were used to build FSM in the study area. To provide inputs into parallel ensemble-based machine learning algorithms, we gathered and processed data from meteorological (Rainfall), satellite image (flood inventory, normalized difference vegetation index (NDVI), aspect, land cover, altitude, stream power index (SPI), plan curvature, topographic wetness index (TWI), slope) and geographic sources (geology). For this research, Sentinel-1 synthetic aperture radar (SAR) satellite images were utilized to locate flooded areas and create an inventory map of floods. To train and validate the model, we employed 70 % and 30 % of 160 selected flood locations, respectively. Multicollinearity, frequency ratio (FR), and Geodetector methods were used for data preprocessing. Four metrics were utilized to assess the FSM performance: the root mean square error (RMSE), the area under the receiver-operator characteristic curve (AUC-ROC), the Taylor diagram, and the seed cell area index (SCAI). The results exhibited that all the suggested models have high accuracy of prediction, but the performance of Bagging-GA (RMSE (Train = 0.1793, Test = 0.4543)) was slightly better than RF-GA (RMSE (Train = 0.1803, Test = 0.4563)), Bagging (RMSE (Train = 0.2191, Test = 0.4566)), and RF (RMSE (Train = 0.2529, Test = 0.4724)). According to the ROC index, the Bagging-GA model (AUC = 0.935) was the most accurate in flood susceptibility modeling, followed by the RF-GA (AUC = 0.904), the Bagging (AUC = 0.872), and the RF (AUC = 0.847) models. The study's identification of high-risk flood zones and the most significant factors contributing to flooding make it a helpful resource for flood management.

Environment and the development of severe asthma in inner city population

PURPOSE OF REVIEW

Higher asthma prevalence and morbidity are seen in inner-city areas, disproportionately affecting low-income families living in substandard housing. Children within these families experience more frequent asthma exacerbations, acute care and emergency department visits, and hospitalizations, thus characterizing severe asthma. In this review, we assess recent published literature focused on indoor and outdoor exposures that contribute to the development and morbidity of asthma.

RECENT FINDINGS

Many urban environmental exposures contribute to asthma burden, including tobacco/e-cigarette smoke, pest allergens, molds, and possibly synthetic chemicals such as phthalates and bisphenol A, radon, and volatile organic compounds. Individuals living in inner-city areas also experience higher levels of air pollutants and ambient heat, further perpetuating asthma incidence and severity.

SUMMARY

This article summarizes the latest advances and provides direction for future research on risk factors, interventions, and public policy to help alleviate the burden of asthma due to urban environment exposures.

Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.

Study on the spatiotemporal dynamic of ground-level ozone concentrations on multiple scales across China during the blue sky protection campaign

Surface ozone (O₃), one of the harmful air pollutants, generated significantly negative effects on human health and plants. Existing O₃ datasets with coarse spatiotemporal resolution and limited coverage, and the uncertainties of O₃ influential factors seriously restrain related epidemiology and air pollution studies. To tackle above issues, we proposed a novel scheme to estimate daily O₃ concentrations on a fine grid scale (1 km × 1 km) from 2018 to 2020 across China based on machine learning methods using hourly observed ground-level pollutant concentrations data, meteorological data, satellite data, and auxiliary data including digital elevation model (DEM), land use data (LUD), normalized difference vegetation index (NDVI), population (POP), and nighttime light images (NTL), and to identify the difference of influential factors of O₃ on diverse urbanization and topography conditions. Some findings were achieved. The correlation coefficients (R²) between O₃ concentrations and surface net solar radiation (SNSR), boundary layer height (BLH), 2 m temperature (T2M), 10 m v-component (MVW), and NDVI were 0.80, 0.40, 0.35, 0.30, and 0.20, respectively. The random forest (RF) demonstrated the highest validation R² (0.86) and lowest validation RMSE (13.74 μg/m³) in estimating O₃ concentrations, followed by support vector machine (SVM) (R² = 0.75, RMSE = 18.39 μg/m³), backpropagation neural network (BP) (R² = 0.74, RMSE = 19.26 μg/m³), and multiple linear regression (MLR) (R² = 0.52, RMSE = 25.99 μg/m³). Our China High-Resolution O₃ Dataset (CHROD) exhibited an acceptable accuracy at different spatial-temporal scales. Additionally, O₃ concentrations showed decreasing trend and represented obviously spatiotemporal heterogeneity across China from 2018 to 2020. Overall, O₃ was mainly affected by human activities in higher urbanization regions, while O₃ was mainly controlled by meteorological factors, vegetation coverage, and elevation in lower urbanization regions. The scheme of this study is useful and valuable in understanding the mechanism of O₃ formation and improving the quality of the O₃ dataset.

Nonlinear associations between environmental factors and lipid levels in middle-aged and elderly population in China: A national cross-sectional study

BACKGROUND

Blood lipid is an important factor affecting cardiovascular disease in middle-aged and elderly people. At present, the associations between environmental factors and blood lipid level in elderly people has been controversial, and the nonlinear effect of their relationship is lack of research.

METHODS

This study used data from a national cross-sectional survey of blood lipid levels in 13,354 subjects and data from environmental monitoring sites. Logistic regression was used to measure the relationship between the basic characteristics of the study population and blood lipid levels. After controlling the confounding factors, the nonlinear associations between environmental factors and blood lipid levels of middle-aged and elderly people in different geographical regions were studied by random forest model.

RESULTS

The risk of dyslipidemia is significantly higher in middle-aged women, obese people, elderly people, and urban people. Smoking and alcohol consumption increase the risk. The associations between environmental factors and lipid levels of middle-aged and elderly people are nonlinear, the correlation effect between air pollutants and blood lipid level is mainly shown in northern China, and the correlation between meteorological factors and blood lipid level is more obvious in southern China.

CONCLUSIONS

This study shows that the associations between environmental factors and lipid levels in middle-aged and elderly population are nonlinear and have regional differences. Therefore it should be considered in optimizing the allocation of public health resources and preventing and controlling environmental exposure of middle-aged and elderly population.

Issue 1 - "Update on adverse respiratory effects of outdoor air pollution" Part 2): Outdoor air pollution and respiratory diseases: Perspectives from Angola, Brazil, Canada, Iran, Mozambique and Portugal

OBJECTIVE

To analyse the GARD perspective on the health effects of outdoor air pollution, and to synthesise the Portuguese epidemiological contribution to knowledge on its respiratory impact.

RESULTS

Ambient air pollution has deleterious respiratory effects which are more apparent in larger, densely populated and industrialised countries, such as Canada, Iran, Brazil and Portugal, but it also affects people living in low-level exposure areas. While low- and middle-income countries (LMICs), are particularly affected, evidence based on epidemiological studies from LMICs is both limited and heterogeneous. While nationally, Portugal has a relatively low level of air pollution, many major cities face with substantial air pollution problems. Time series and cross-sectional epidemiological studies have suggested increased respiratory hospital admissions, and increased risk of respiratory diseases in people who live in urban areas and are exposed to even a relatively low level of air pollution.

CONCLUSIONS

Adverse respiratory effects due to air pollution, even at low levels, have been confirmed by epidemiological studies. However, evidence from LMICs is heterogeneous and relatively limited. Furthermore, longitudinal cohort studies designed to study and quantify the link between exposure to air pollutants and respiratory diseases are needed. Worldwide, an integrated approach must involve multi-level stakeholders including governments (in Portugal, the Portuguese Ministry of Health, which hosts GARD-Portugal), academia, health professionals, scientific societies, patient associations and the community at large. Such an approach not only will garner a robust commitment, establish strong advocacy and clear objectives, and raise greater awareness, it will also support a strategy with adequate measures to be implemented to achieve better air quality and reduce the burden of chronic respiratory diseases (CRDs).

Spatio-temporal modeling of PM2.5 risk mapping using three machine learning algorithms

Urban air pollution is one of the most critical issues that affect the environment, community health, economy, and management of urban areas. From a public health perspective, PM_2.5 is one of the primary air pollutants, especially in Tehran's metropolis. Owing to the different patterns of PM_2.5 in different seasons, Spatio-temporal modeling and identification of high-risk areas to reduce its effects seems necessary. The purpose of this study was Spatio-temporal modeling and preparation of PM_2.5 risk mapping using three machine learning algorithms (random forest (RF), AdaBoost, and stochastic gradient descent (SGD)) in the metropolis of Tehran, Iran. Therefore, in the first step, to prepare the dependent variable data, the PM_2.5 average was used for the four seasons of spring, summer, autumn, and winter. Then, using remote sensing (RS) and a geographic information system (GIS), independent data such as temperature, maximum temperature, minimum temperature, wind speed, rainfall, humidity, normalized difference vegetation index (NDVI), population density, street density, and distance to industrial centers were prepared as a seasonal average. To Spatio-temporal modeling using machine learning algorithms, 70% of the data were used for training and 30% for validation. The frequency ratio (FR) model was used as input to machine learning algorithms to calculate the spatial relationship between PM_2.5 and the effective parameters. Finally, Spatio-temporal modeling and PM_2.5 risk mapping were performed using three machine learning algorithms. The receiver operating characteristic (ROC) area under the curve (AUC) results showed that the RF algorithm had the greatest modeling accuracy, with values of 0.926, 0.94, 0.949, and 0.949 for spring, summer, autumn, and winter, respectively. According to the RF model, the most important variable in spring and autumn was NDVI. Temperature and distance to industrial centers were the most important variables in the summer and winter, respectively. The results showed that autumn, winter, summer, and spring had the highest risk of PM_2.5, respectively.

COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms

The reduction of population concentration in some urban land uses is one way to prevent and reduce the spread of COVID-19 disease. Therefore, the objective of this study is to prepare the risk mapping of COVID-19 in Tehran, Iran, using machine learning algorithms according to socio-economic criteria of land use. Initially, a spatial database was created using 2282 locations of patients with COVID-19 from 2 February 2020 to 21 March 2020 and eight socio-economic land uses affecting the disease-public transport stations, supermarkets, banks, automated teller machines (ATMs), bakeries, pharmacies, fuel stations, and hospitals. The modeling was performed using three machine learning algorithms that included random forest (RF), adaptive neuro-fuzzy inference system (ANFIS), and logistic regression (LR). Feature selection was performed using the OneR method, and the correlation between land uses was obtained using the Pearson coefficient. We deployed 70% and 30% of COVID-19 patient locations for modeling and validation, respectively. The results of the receiver operating characteristic (ROC) curve and the area under the curve (AUC) showed that the RF algorithm, which had a value of 0.803, had the highest modeling accuracy, which was followed by the ANFIS algorithm with a value of 0.758 and the LR algorithm with a value of 0.747. The results showed that the central and the eastern regions of Tehran are more at risk. Public transportation stations and pharmacies were the most correlated with the location of COVID-19 patients in Tehran, according to the results of the OneR technique, RF, and LR algorithms. The results of the Pearson correlation showed that pharmacies and banks are the most incompatible in distribution, and the density of these land uses in Tehran has caused the prevalence of COVID-19.

Spatial Modeling of Asthma-Prone Areas Using Remote Sensing and Ensemble Machine Learning Algorithms

In this study, asthma-prone area modeling of Tehran, Iran was provided by employing three ensemble machine learning algorithms (Bootstrap aggregating (Bagging), Adaptive Boosting (AdaBoost), and Stacking). First, a spatial database was created with 872 locations of asthma patients and affecting factors (particulate matter (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), rainfall, wind speed, humidity, temperature, distance to street, traffic volume, and a normalized difference vegetation index (NDVI)). We created four factors using remote sensing (RS) imagery, including air pollution (O3, SO2, CO, and NO2), altitude, and NDVI. All criteria were prepared using a geographic information system (GIS). For modeling and validation, 70% and 30% of the data were used, respectively. The weight of evidence (WOE) model was used to assess the spatial relationship between the dependent and independent data. Finally, three ensemble algorithms were used to perform asthma-prone areas mapping. According to the Gini index, the most influential factors on asthma occurrence were distance to the street, NDVI, and traffic volume. The area under the curve (AUC) of receiver operating characteristic (ROC) values for the AdaBoost, Bagging, and Stacking algorithms was 0.849, 0.82, and 0.785, respectively. According to the findings, the AdaBoost algorithm outperforms the Bagging and Stacking algorithms in spatial modeling of asthma-prone areas.