Air Pollution Model Research Articles

A novel statistical modeling of air pollution and the COVID-19 pandemic mortality data by Poisson, geometric, and negative binomial regression models with fixed and random effects

Abstract The coronavirus disease 2019 (COVID-19) pandemic was defined by the World Health Organization (WHO) as a global epidemic on March 11, 2020, as the infectious disease that threatens public health fatally. In this study, the main aim is to model the impact of various air pollution causes on mortality data due to the COVID-19 pandemic by Generalized Linear Mixed Model (GLMM) approach to make global statistical inferences about 174 WHO member countries as subjects in the six WHO regions. “Total number of deaths by these countries due to the COVID-19 pandemic” until July 27, 2022, is taken as the response variable. The explanatory variables are taken as the WHO regions, the number of deaths from air pollution causes per 100.000 population as “household air pollution from solid fuels,” “ambient particulate matter pollution,” and “ambient ozone pollution.” In this study, Poisson, geometric, and negative binomial (NB) regression models with “country” taken as fixed and random effects, as special cases of GLMM, are fitted to model the response variable in the aspect of the above-mentioned explanatory variables. In the Poisson, geometric, and NB regression models, Iteratively Reweighted Least Squares parameter estimation method with the Fisher-Scoring iterative algorithm under the log-link function as canonical link function is used. In the GLMM approach, Laplace approximation is also used in the prediction of random effects. In this study, six different Poisson, geometric, and NB regression models with fixed and random effects are established for 174 countries all over the world to make global statistical inferences for investigating the relationships between “total number of deaths” by these countries due to the COVID-19 pandemic and “air pollution causes.” As a result of this study, “NB mixed-effects regression model” as the most appropriate GLMM is used to make global statistical inferences about the impact of the various air pollution causes on the mortality data due to the COVID-19 pandemic.

Spatial-temporal evaluation of PM2.5 concentration for health risk reduction strategy development in a basin with different weather patterns

Identifying the contribution of pollution sources to the atmospheric environment is critical to make an effective pollution reduction strategy for controlling the regional air quality. Spatial-temporal distribution of pollutant patterns is not only determined by the emission rate of a pollution source, but also closely related to the location of an emission source, regional topography, and wind field conditions. To understand the spatial pattern of pollutants under different weather conditions and topographical factors, this study evaluated the impact of large-scale stationary pollution sources in Taichung Basin located in Central Taiwan on regional air quality using The Air Pollution Model (TAPM), in which different weather types were taken into considerations. Results show that the present TAPM model could accurately simulate 44.0–95.0% of 5 actual synoptic weather conditions, including Outer-region circulation from tropical depression system, Weak northeast monsoon, Anticyclonic Outflow, The westward extension of Pacific Anticyclone, and South-deflected airflow. The research results revealed that weather types and regional wind fields dominated the pollutant transport patterns, where the westward extension of the Pacific Anticyclone and South-deflected Airflow could deteriorate the convection-diffusive conditions, causing high pollution events for Taichung Basin. Based on the present results, the air pollutants transport patterns under different synoptic weather conditions could be used as practical information by decision-makers to develop efficient air quality control strategies in the future.

Urban-scale variational flux inversion for CO Using TROPOMI total-column retrievals: A case study of Tehran

Emission inventories of trace gases and aerosols are a key input for air pollution modelling, yet are subject to significant uncertainties. The aim of this study is to refine urban-scale, gridded estimates of carbon monoxide (CO) emissions for Tehran, Iran; this was done using an inverse-modelling approach. We assimilated total column mixing ratios of CO retrieved from the TROPOspheric Monitoring Instrument (TROPOMI) using a four-dimensional variational assimilation system based on the Community Multi-Scale Air Quality Model. The inversion system allows for two representations of the temporal profile of emissions at each grid-point: scaling a fixed diurnal profile, or with separate, constant estimates spanning four diurnal time-categories. We evaluate the inversion capability by applying independent surface measurements.We find that the model forced with prior emissions substantially underestimates day-to-day variation in satellite data. However, the posterior emissions show significant improvements in simulating the assimilated data, reducing the bias of modelled mixing ratios by 72% to 91% when using categorised temporal variation in the system and by 35% to 83% with a fixed temporal variation. Comparisons with surface measurements indicate that the posterior emissions also show some improvements in simulating surface observations, although not in all cases. For example, in February 2019, the magnitude of the bias is reduced from 1.1 ppm to 0.9 ppm and 0.2 ppm using the categorised and fixed temporal variation schemes, respectively. The posterior results are sensitive to the specification of temporal emission patterns, and the spatiotemporal pattern of the posterior varies depending on whether we assume the fixed or categorised temporal variation. It is worth noting that TROPOMI samples only once per day, so it cannot define the full diurnal cycle of emissions. Finally, the inability to match simultaneously the surface and column-integrated observations may be due to issues in modelling the vertical tracer transport, however, this was not evaluated in the present work. Notwithstanding these weaknesses, the approach shows considerable promise for regions lacking local inventories.

Possibilistic index-based multi-criteria decision-making with an unknown weight of air pollution model under bipolar fuzzy environment

Possibilistic index-based multi-criteria decision-making with an unknown weight of air pollution model under bipolar fuzzy environment

Air emission inventory and air quality modelling for developing countries: A case of Vinh Phuc province, Vietnam

Vinh Phuc is a province in the Northern Key Economic Region with a developed industry, a high population rate, and a relatively high traffic density. Along with economic growth, environmental protection in general, and air quality improvement in particular are two challenges that Vinh Phuc authorities must identify in the coming years. To do so, total emission load data must be acquired in order to establish a pollution distribution map for the entire Vinh Phuc province. The paper’s aim is to provide: (i) air emissions inventory, and (ii) the application of the model to build a map of pollution distribution in Vinh Phuc province. Different methods are used in this study, including (i) emission inventory utilizing both bottom-up and top-down techniques [1], (ii) air quality simulation applying the Finite Volume Model - Transport and Air Pollution Model [2] (FVM-TAPOM), and (iii) other approaches such as interview method, statistical tool, geographic information system (GIS), expert method, etc. According to research findings, motorbikes account for more than 90% of the concentration of pollutants for traffic activities. Iron and steel industries has the highest emission among 15 industries and straw burning activities account for more than 70% of total area source emissions. An emissions distribution map for traffic and industry sources was developed based on air quality simulation which showed that Vinh Yen city has the highest emissions among 09 districts and cities in Vinh Phuc province. The study also identified the causes of air pollution in Vinh Phuc province, providing remedies for the management agency to release plans and activities to minimize Vinh Phuc province emissions and ensure the local community’s air quality and health.

Modeling and Analysis of Air Pollution and Environmental Justice: The Case for North Carolina's Hog Concentrated Animal Feeding Operations.

Concentrated animal feeding operations (CAFOs) emit pollutants that can cause negative impacts on human health. The concentration of hog production in North Carolina raises concerns regarding the disproportionate exposure of vulnerable communities to air pollution from CAFOs. We investigated whether exposure to gaseous ammonia () and hydrogen sulfide () (in 2019) differs between subpopulations by examining demographics, including race/ethnicity, age, educational attainment, language proficiency, and socioeconomic status. We used an Air Monitoring Station (AMS)/Environmental Protection Agency (EPA) Regulatory Model (AERMOD)-based Human Exposure Model (version 3) to estimate ambient concentrations of and from hog farms in Duplin County and its surrounding counties in North Carolina and estimate subsequent exposures of communities within of Duplin County, North Carolina, or the Duplin County Region. We combined estimated exposures with 2016 American Community Summary Census data, at the block group level, using spatial analysis to investigate whether exposures to these pollutants differ by race and ethnicity, age, income, education, and language proficiency. Based on these estimations, we assessed associated exposure risks to the impacted communities and used multivariable regression modeling to evaluate the relationship between average ammonia exposures from Duplin regional hog farms and the presence of vulnerable populations. The average [] annual estimated concentration of and in the Duplin County Region is and , respectively. The maximum average annual ambient concentrations are estimated at and for and , respectively. Our descriptive analysis reveals that people of low income, people of color, people with low educational attainment, and the linguistically isolated in the Duplin Region are disproportionately exposed to higher levels of pollutants than the average exposure for residents. Alternatively, our statistical results suggests that after adjusting for covariates, communities of color are associated with 1.70% (95% CI: , 0.44) lower concentrations per 1-SD increase. One-standard deviation increases in the adults with low educational attainment and children years of age is associated with 1.26% (95% CI: , 3.33) and 1.20% (95% CI: , 3.05) higher exposure per 1-SD increase, respectively. Exposures to and differed by race and ethnicity, educational attainment, language proficiency, and socioeconomic status. The observed associations between exposure to CAFO-generated pollutants and sociodemographic indicators differed among demographics. The disproportionate distribution of hog facilities and resulting pollutant exposures among communities may have adverse environmental and human health impacts, raising environmental justice concerns. https://doi.org/10.1289/EHP11344.

Local Galerkin Method Based on the Moving Least Squares Approximation for Solving Delay Integral Equations Arisen from an Air Pollution Model

Mathematical models for measuring pollutants, by predicting the amount of air quality elements, play an important role to protect the human health. As one of these models, delay Volterra integral equations are applied to simulate a network of sensors with past memory to evaluate the emissions of pollutants in the air. This paper presents a computational method to solve these types of delay integral equations using the discrete Galerkin scheme together with the moving least squares (MLS) approach as basis. The MLS is an effective technique to estimate an unknown function which includes a locally weighted least squares polynomial fitting over a small set of all points. The composite Gauss–Legendre quadrature formula is utilized to compute integrals appearing in the proposed method. Since the scheme is constructed on a local scattered data approximation, its algorithm is attractive and easy to run on a computer with normal features. The error estimation and convergence rate of the method are provided. Finally, numerical examples illustrate the efficiency and accuracy of the new technique and confirm the theoretical results obtained in the error analysis.

Optimizing Air Pollution Modeling with a Highly-Convergent Quasi-Monte Carlo Method: A Case Study on the UNI-DEM Framework

In this study, we present the development of an advanced air pollution modeling approach, which incorporates cutting-edge stochastic techniques for large-scale simulations of long-range air pollutant transportation. The Unified Danish Eulerian Model (UNI-DEM) serves as a crucial mathematical framework with numerous applications in studies concerning the detrimental effects of heightened air pollution levels. We employ the UNI-DEM model in our research to obtain trustworthy insights into critical questions pertaining to environmental preservation. Our proposed methodology is a highly convergent quasi-Monte Carlo technique that relies on a unique symmetrization lattice rule. By fusing the concepts of special functions and optimal generating vectors, we create a novel algorithm grounded in the component-by-component construction method, which has been recently introduced. This amalgamation yields particularly impressive outcomes for lower-dimensional cases, substantially enhancing the performance of the most advanced existing methods for calculating the Sobol sensitivity indices of the UNI-DEM model. This improvement is vital, as these indices form an essential component of the digital ecosystem for environmental analysis.

Long-term exposure to air pollution and cognitive function in older adults: a systematic review and meta-analysis

BackgroundNow more than ever before, air pollution and cognitive decline are global concerns. Credible links are now emerging between exposure to specific pollutants and cognitive decline. However, the role of specific pollutants on different cognitive domains in adults are inconclusive as the pathways to cognitive decline remain poorly understood.ObjectiveTo systematically review and meta-analyse the evidence for the association between long-term air pollution exposure and cognitive function in adults.Data sourcesWeb of Science, PubMed, Embase searched up to February 2021 with no language restrictions. Individual studies were identified from similar review articles.Eligibility criteriaStudies investigating the long-term (>3 years) associations between airborne pollutants and cognitive function in older adults (>50 years old).ResultsFrom 1996 records, 26 satisfied the inclusion criteria. The total sample size included over 2.4 million (53.8% female) subjects with ages ranging from 45 to 100 years (estimated mean age 70 years). Only 18/26 publications included both male and female subjects. Pollutants reported included particulate matter ≤2.5 µm (PM2.5); particulate matter ≤10 µm (PM10); nitrogen dioxide (NO2); nitrogen oxide species (NOx) and ozone (O3). Papers showed great variance in their study characteristics, air pollution modelling methodology and assessment of cognitive domain. Long-term exposure to increased levels of PM2.5 and NO2 were most strongly associated with increased risk of dementia. Decline in cognitive function, executive function, memory and language were most strongly associated with greater exposure to PM2.5, PM10 and NO2 to varying degrees.ConclusionAn increasing number of studies are supporting the hypothesis that greater exposure to air pollutants is associated with decline in cognitive functions.

Advances in Causal Inference at the Intersection of Air Pollution and Health Outcomes

This article provides an overview of the recent economics literature analyzing the effect of air pollution on health outcomes. We review the common approaches to measuring and modeling air pollution exposures and the epidemiological and biological literature on health outcomes that undergird federal air regulations in the United States. The article contrasts the methods used in the epidemiology literature with the causal inference framework used in economics. In particular, we review the common sources of estimation bias in epidemiological approaches that the economics literature has sought to overcome with research designs that take advantage of natural experiments. We review new promising research designs for estimating concentration-response functions and identify areas for further research.

Medium term moderate to low-level air pollution exposure is associated with higher C-reactive protein among healthy Danish blood donors

Air pollution is a significant contributor to the global burden of disease with a plethora of associated health effects such as pulmonary and systemic inflammation. C-reactive protein (CRP) is associated with a wide range of diseases and is associated with several exposures. Studies on the effect of air pollution exposure on CRP levels in low to moderate pollution settings have shown inconsistent results.In this cross-sectional study high sensitivity CRP measurements on 18,463 Danish blood donors were linked to modelled air pollution data for NOx, NO2, O3, CO, SO2, NH3, mineral dust, black carbon, organic carbon, sea salt, secondary inorganic aerosols and its components, primary PM2.5, secondary organic aerosols, total PM2.5, and total PM10 at their residential address over the previous month. Associations were analysed using ordered logistic regression with CRP quartile as individuals outcome and air pollution exposure as scaled deciles. Analyses were adjusted for health related and socioeconomic covariates using health questionnaires and Danish register data.Exposure to different air pollution components was generally associated with higher CRP (odds ratio estimates ranging from 1.11 to 1.67), while exposure to a few air pollution components was associated with lower CRP. For example, exposure to NO2 increased the odds of high CRP 1.32-fold (95%CI 1.16–1.49), while exposure to NH3 decreased the odds of high CRP 0.81-fold (95%CI 0.73–0.89).This large study among healthy individuals found air pollution exposure to be associated with increased levels of CRP even in a setting with low to moderate air pollution levels.

Burden of fine air pollution on mortality in the desert climate of Kuwait

BackgroundMiddle Eastern desert countries like Kuwait are known for intense dust storms and enormous petrochemical industries affecting ambient air pollution. However, local health authorities have not been able to assess the health impacts of air pollution due to limited monitoring networks and a lack of historical exposure data.ObjectiveTo assess the burden of PM2.5 on mortality in the understudied dusty environment of Kuwait.MethodsWe analyzed the acute impact of fine particulate matter (PM2.5) on daily mortality in Kuwait between 2001 and 2016. To do so, we used spatiotemporally resolved estimates of PM2.5 in the region. Our analysis explored factors such as cause of death, sex, age, and nationality. We fitted quasi-Poisson time-series regression for lagged PM2.5 adjusted for time trend, seasonality, day of the week, temperature, and relative humidity.ResultsThere was a total of 70,321 deaths during the study period of 16 years. The average urban PM2.5 was estimated to be 46.2 ± 19.8 µg/m3. A 10 µg/m3 increase in a 3-day moving average of urban PM2.5 was associated with 1.19% (95% CI: 0.59, 1.80%) increase in all-cause mortality. For a 10 µg/m3 reduction in annual PM2.5 concentrations, a total of 52.3 (95% CI: 25.7, 79.1) deaths each year could be averted in Kuwait. That is, 28.6 (95% CI: 10.3, 47.0) Kuwaitis, 23.9 (95% CI: 6.4, 41.5) non-Kuwaitis, 9.4 (95% CI: 1.2, 17.8) children, and 20.9 (95% CI: 4.3, 37.6) elderly deaths each year.Impact StatementThe overwhelming prevalence of devastating dust storms and enormous petrochemical industries in the Gulf and the Middle East has intensified the urgency to address air pollution and its detrimental health effects. Alarmingly, the region’s epidemiological research lags behind, hindered by a paucity of ground monitoring networks and historical exposure data. In response, we are harnessing the power of big data to generate predictive models of air pollution across time and space, providing crucial insights into the mortality burden associated with air pollution in this under-researched yet critically impacted area.

Air pollutant diffusion trend prediction based on deep learning for targeted season—North China as an example

The air pollutant diffusion trend prediction plays an important role in the environment protection. The approaches in the existing studies rarely consider spatial and temporal information sequentially or cannot use spatiotemporal structure information effectively. In this paper, we propose a new deep learning-based air pollutant diffusion trend prediction model, called GATBL-Learning. It is an end-to-end neural network model consisting of Graph Attention Network (GAT) and Encoder-Decoder model. Since each city in the region will set as a node, GAT can extract spatial information from them by aggregation operation. Then Encoder-Decoder model, consisting of Bi-LSTM as Encoder and LSTM as Decoder, mines time series features and outputs the data containing spatiotemporal features. In addition, we also observe that the changes of air pollutants show seasonality discipline and training the seasonal model can improve the model adaptability. The results demonstrate the proposed prediction model is effective and superior.

Accounting for Area Sources in Air Pollution Models.

Area sources are important components of comprehensive air pollution models. The literature describes several approaches to modeling dispersion from such sources, but there is little consensus on an approach that can be applied to arbitrarily shaped area sources and is numerically efficient at the same time. This paper brings together ideas from previous work to propose an approach that meets these requirements. It is based on representing an area source as a set of line sources perpendicular to the wind direction; the number of line sources is determined by the specified precision of the concentration computed at a receptor impacted by the area source. Although AERMOD and the OML model incorporate versions of this approach, the open literature lacks an adequate description. This paper fills this important gap and also provides examples of its application. We show that different shaped area sources with the same emissions and emission density yield significantly different downwind concentration patterns. We then demonstrate the utility of the method through inverse modeling to estimate methane emissions from manure lagoons located in a dairy.

Mass-Transfer Air Pollution Modeling in Heritage Buildings

Two simple mass-balance models for estimating the concentration of air pollutants inside buildings are presented for pollutants originating from outdoors or generated indoors. The models can be used to establish average pollution loads on heritage objects inside buildings and assist in risk assessment for conservation. The models can be run with a minimum of data, either based on fixed conditions or as a Monte Carlo simulation based on plausible intervals of the input factors. Input data can be obtained by simple measurements or based on the literature. A museum storage hall in Denmark was used as a test site for demonstrating the models. They were evaluated with regard to the prediction of the indoor/outdoor concentration ratio for ozone and nitrogen dioxide and the build-up concentration of indoor generated organic acids. The pros and cons of such models were discussed, where the main reservation is related to shortcomings when real buildings are more complicated than the single-zone structure of the models. A strength of the models is the easy adaption to an indoor environment and, despite being semi-quantitative at times, the simplicity of the models, which allows for practical everyday use in air quality management of heritage buildings.

The role of metabolic perturbations in mediating the effects of ambient air pollution on lung cancer in the cancer prevention studies.

10541 Background: Exposure to ambient air pollution is an established risk factor for lung cancer. Investigation on the underlying mechanisms of air pollution carcinogenicity remains challenging due to the complex composition of air pollution and a lack of sensitive biomarkers to accurately measure exposure and corresponding response. To address the knowledge gaps, we applied high-resolution metabolomics to identify metabolic signatures of exogenous air pollution exposures and endogenous processes involved in lung carcinogenesis. Methods: A total of 683 matched lung cancer and control pairs within the established Cancer Prevention Study-II (CPS-II) Nutrition and CPS-3 cohorts were included in this study. The plasma metabolome was profiled with ultrahigh-performance liquid chromatography-tandem mass spectrometry. Assessment of exposure to six ambient air pollutants, including carbon monoxide (CO), nitrogen dioxide (NO2), particulate matter (PM10), fine particulate matter (PM2.5), sulfur dioxide (SO2), and ozone (O3), was conducted using spatiotemporally-resolved models based on residential address at blood draw and calculated based on yearly average levels. We conducted metabolome-wide association studies with multiple linear regression models to assess associations of ambient air pollution and lung cancer with a meet-in-the-middle approach. Models controlled for potential confounders and covariates including age at blood draw, sex, race, body mass index, alcohol consumption, smoking status, passive smoke exposure, vegetable and fruit intake, and educational level. Metabolites significant at the FDR < 0.2 level in the air pollution model were analyzed in the lung cancer model. High-dimensional mediation analysis was used as a secondary analysis to compare results from the meet-in-the-middle analysis. Results: Among 1,204 metabolic features extracted from the blood samples, seven features were significantly associated with air pollution exposure and lung cancer incidence at the FDR < 0.2 level in the meet-in-the-middle analysis. Six features were significant at the p < 0.01 level through high-dimensional mediation analysis. All confirmed metabolites are enriched within peptide, lipid, and amino acid pathways. Gamma-glutamylglutamine and gamma-glutamylmethionine were statistically significantly associated with CO, NO2, and PM10 exposure and lung cancer incidence in both analyses. Conclusions: This is the largest prospective metabolomics study examining biological perturbations associated with air pollution exposure and lung cancer incidence. Peptide metabolism may play an important role in mediating the association between air pollution and lung cancer. Findings from this study will lay the foundation for future studies to further understand the biological mechanisms underlying the carcinogenicity of air pollution.

Reduced order model for simulation of air pollution model and application in 2D urban street canyons via the meshfree gradient smoothing method

Reduced order model for simulation of air pollution model and application in 2D urban street canyons via the meshfree gradient smoothing method

Monitoring ambient air pollution and pulmonary function in asthmatic children by mobile applications in COVID-19 pandemic.

Several public health measures were implemented during the COVID-19 pandemic. However, little is known about the real-time assessment of environmental exposure on the pulmonary function of asthmatic children. Therefore, we developed a mobile phone application for capturing real-time day-to-day dynamic changes in ambient air pollution during the pandemic. We aim to explore the change in ambient air pollutants between pre-lockdown, lockdowns, and lockdowns and analyze the association between pollutants and PEF mediated by mite sensitization and seasonal change. A prospective cohort study was conducted among 511 asthmatic children from January 2016 to February 2022. Smartphone-app used to record daily ambient air pollution, particulate matter (PM2.5, PM10) Ozon (O3), nitrogen dioxide (NO2), Carbon Monoxide (CO), sulfur dioxide (SO2), average temperature, and relative humidity, which measured and connected from 77 nearby air monitoring stations by linking to Global Positioning System (GPS)-based software. The outcome of pollutants' effect on peak expiratory flow meter (PEF) and asthma is measured by a smart peak flow meter from each patient or caregiver's phone for real-time assessment. The lockdown (May 19th, 2021, to July 27th, 2021) was associated with decreased levels of all ambient air pollutants aside from SO2 after adjusting for 2021. NO2 and SO2 were constantly associated with decreased levels of PEF across lag 0 (same day when the PEF was measured), lag 1 (one day before PEF was measured), and lag 2 (two days prior when the PEF was measured. Concentrations of CO were associated with PEF only in children who were sensitized to mites in lag 0, lag 1, and lag 2 in the stratification analysis for a single air pollutant model. Based on the season, spring has a higher association with the decrease of PEF in all pollutant exposure than other seasons. Using our developed smartphone apps, we identified that NO2, CO, and PM10 were higher at the pre-and post-COVID-19 lockdowns than during the lockdown. Our smartphone apps may help collect personal air pollution data and lung function, especially for asthmatic patients, and may guide protection against asthma attacks. It provides a new model for individualized care in the COVID era and beyond.

Long-term ambient air pollution and venous thromboembolism in a population-based Swedish cohort

Air pollution is a major contributor to the global burden of disease and has been linked to several diseases and conditions, including cardiovascular disease. The biological mechanisms are related to inflammation and increased coagulability, factors that play an important role in the pathogenesis of venous thromboembolism (VTE, i.e., deep vein thrombosis or pulmonary embolism). This study investigates if long-term exposure to air pollution is associated with increased VTE incidence. The study followed 29 408 participants from the Malmö Diet and Cancer (MDC) cohort, which consists of adults aged 44–74 recruited in Malmö, Sweden between 1991 and 1996. For each participant, annual mean residential exposures to particulate matter <2.5 μg (PM2.5) and <10 μg (PM10), nitrogen oxides (NOx) and black carbon (BC) from 1990 up to 2016 were calculated. Associations with VTE were analysed using Cox proportional hazard models for air pollution in the year of the VTE event (lag0) and the mean of the prior 1–10 years (lag1-10). Annual air pollution exposures for the full follow-up period had the following means: 10.8 μg/m3 for PM2.5, 15.8 μg/m3 for PM10, 27.7 μg/m3 for NOx, and 0.96 μg/m3 for BC. The mean follow-up period was 19.5 years, with 1418 incident VTE events recorded during this period. Exposure to lag1-10 PM2.5 was associated with an increased risk of VTE (HR 1.17 (95%CI 1.01–1.37)) per interquartile range (IQR) of 1.2 μg/m3 increase in PM2.5 exposure. No significant associations were found between other pollutants or lag0 PM2.5 and incident VTE. When VTE was divided into specific diagnoses, associations with lag1-10 PM2.5 exposure were similarly positive for deep vein thrombosis but not for pulmonary embolism. Results persisted in sensitivity analyses and in multi-pollutant models. Long-term exposure to moderate concentrations of ambient PM2.5 was associated with increased risks of VTE in the general population in Sweden.

A comparison of machine learning methods for ozone pollution prediction

Precise and efficient ozone (hbox {O}_{3}) concentration prediction is crucial for weather monitoring and environmental policymaking due to the harmful effects of high hbox {O}_{3} pollution levels on human health and ecosystems. However, the complexity of hbox {O}_{3} formation mechanisms in the troposphere presents a significant challenge in modeling hbox {O}_{3} accurately and quickly, especially in the absence of a process model. Data-driven machine-learning techniques have demonstrated promising performance in modeling air pollution, mainly when a process model is unavailable. This study evaluates the predictive performance of nineteen machine learning models for ozone pollution prediction. Specifically, we assess how incorporating features using Random Forest affects hbox {O}_{3} concentration prediction and investigate using time-lagged measurements to improve prediction accuracy. Air pollution and meteorological data collected at King Abdullah University of Science and Technology are used. Results show that dynamic models using time-lagged data outperform static and reduced machine learning models. Incorporating time-lagged data improves the accuracy of machine learning models by 300% and 200%, respectively, compared to static and reduced models, under RMSE metrics. And importantly, the best dynamic model with time-lagged information only requires 0.01 s, indicating its practical use. The Diebold-Mariano Test, a statistical test used to compare the forecasting accuracy of models, is also conducted.