In the western United States, there has been a significant increase in both the size and number of wildfires associated with the increasing drought conditions. The six largest wildfires in U.S. history have occurred in the last seven years. Four of the six fires were in California (2017 Tubbs Fire, 2018 Camp Fire, 2020 Bay Area Fire, and 2021 Dixie Fire), where the mountains create complex atmospheric flows that lead to terrain-induced wildfires from dry, downslope winds. To investigate the health outcomes associated with smoke exposure the spatiotemporal distribution of smoke plume concentrations is required. Air quality monitors are sparse and do not quantify pollutant concentrations solely from smoke plumes. To estimate smoke impacts, the monitoring data can be supplemented with information from satellites, wildfire emissions inventories, dispersion models, and chemical transport models. Results of air quality modeling efforts to simulate wildfire smoke plume transport in the western U.S. and estimate smoke exposure are presented here. The focus of this work is on two recent modeling efforts (1) a novel smoke exposure modeling framework that provides estimates by fuel type, fire size, and plume age and (2) gap-filling approaches that leverage machine learning algorithms to increase the usability of satellite aerosol remote sensing products for estimating wildfire smoke exposure.
Random disturbances such as air pollution may affect cognitive performance, which, particularly in high-stakes settings, may have severe consequences for an individual's productivity and well-being. This paper examines the short-term effects of air pollution on school leaving exam results in Poland. I exploit random variation in air pollution between the days on which exams are held across three consecutive school years. I aim to capture this random variation by including school and time fixed effects. The school-level panel data is drawn from a governmental program where air pollution is continuously measured in the schoolyard. This localized hourly air pollution measure is a unique feature of my study, which increases the precision of the estimated effects. In addition, using distant and aggregated air pollution measures allows me for the comparison of the estimates in space and time. The findings suggest that a one standard deviation increase in the concentration of particulate matter PM2.5 and PM10 decreases students' exam scores by around 0.07-0.08 standard deviations. The magnitude and significance of these results depend on the location and timing of the air pollution readin
When exposure measurement error (EME), confounder measurement error (CME), or both are present, health effect estimates regarding exposure mixtures and critical exposure time-window may not represent the true effects. For example, in air pollution epidemiology, modeled estimates for multiple air pollutants and meteorological factors may serve as surrogates for exposures and confounders. Methods for simultaneously addressing EME and CME remain understudied. We developed a two-stage causal effect modeling framework to estimate average exposure/treatment effects (AEE) by addressing EME and CME. We identified conditions under which AEE is identifiable with minimal bias given linear or non-linear potential outcomes models and developed a new method, referred to as multi-dimensional regression calibration (MRC). The first stage of the framework estimates MRC models. The second stage estimates AEE by using g-computation with MR-Calibrated variables. Simulation analyses confirmed the bias-correction capability. As an application, we analyzed the association between air pollution and COVID-19 mortality in Cook County, Illinois. We developed machine learning-based 500m-gridded daily estimate
According to the World Health Organization (WHO), air pollution kills seven million people every year. Outdoor air pollution is a major environmental health problem affecting low, middle, and high-income countries. In the past few years, the research community has explored IoT-enabled machine learning applications for outdoor air pollution prediction. The general objective of this paper is to systematically review applications of machine learning and Internet of Things (IoT) for outdoor air pollution prediction and the combination of monitoring sensors and input features used. Two research questions were formulated for this review. 1086 publications were collected in the initial PRISMA stage. After the screening and eligibility phases, 37 papers were selected for inclusion. A cost-based analysis was conducted on the findings to highlight high-cost monitoring, low-cost IoT and hybrid enabled prediction. Three methods of prediction were identified: time series, feature-based and spatio-temporal. This review's findings identify major limitations in applications found in the literature, namely lack of coverage, lack of diversity of data and lack of inclusion of context-specific feature
Recently, air pollution is one of the most concerns for big cities. Predicting air quality for any regions and at any time is a critical requirement of urban citizens. However, air pollution prediction for the whole city is a challenging problem. The reason is, there are many spatiotemporal factors affecting air pollution throughout the city. Collecting as many of them could help us to forecast air pollution better. In this research, we present many spatiotemporal datasets collected over Seoul city in Korea, which is currently much suffered by air pollution problem as well. These datasets include air pollution data, meteorological data, traffic volume, average driving speed, and air pollution indexes of external areas which are known to impact Seoul's air pollution. To the best of our knowledge, traffic volume and average driving speed data are two new datasets in air pollution research. In addition, recent research in air pollution has tried to build models to interpolate and predict air pollution in the city. Nevertheless, they mostly focused on predicting air quality in discrete locations or used hand-crafted spatial and temporal features. In this paper, we propose the usage of
Air pollution remains a critical environmental and public health challenge, demanding high-resolution spatial data to better understand its spatial distribution and impacts. This study addresses the challenges of conducting multivariate spatial analysis of air pollutants observed at aggregated levels, particularly when the goal is to model the underlying continuous processes and perform spatial predictions at varying resolutions. To address these issues, we propose a continuous multivariate spatial model based on Gaussian processes (GPs), naturally accommodating the support of aggregated sampling units. Computationally efficient inference is achieved using R-INLA, leveraging the connection between GPs and Gaussian Markov random fields (GMRFs). A custom projection matrix maps the GMRFs defined on the triangulation of the study region and the aggregated GPs at sampling units, ensuring accurate handling of changes in spatial support. This approach integrates shared information among pollutants and incorporates covariates, enhancing interpretability and explanatory power. This approach is used to downscale PM2.5, PM10 and ozone levels in Portugal and Italy, improving spatial resolution
Air pollution is the origination of particulate matter, chemicals, or biological substances that brings pain to either humans or other living creatures or instigates discomfort to the natural habitat and the airspace. Hence, air pollution remains one of the paramount environmental issues as far as metropolitan cities are concerned. Several air pollution benchmarks are even said to have a negative influence on human health. Also, improper detection of air pollution benchmarks results in severe complications for humans and living creatures. To address this aspect, a novel technique called, Discretized Regression and Least Square Support Vector (DR-LSSV) based air pollution forecasting is proposed. The results indicate that the proposed DR-LSSV Technique can efficiently enhance air pollution forecasting performance and outperforms the conventional machine learning methods in terms of air pollution forecasting accuracy, air pollution forecasting time, and false positive rate.
Light and air pollution are the two main forms of pollution, containing the highest concentration in urban areas. I set out to investigate the effects of anthropogenic air and light pollution on the night sky, how this affects the astronomical data-collecting process, and how the general public perceives both forms of pollution. This paper utilizes primary and secondary research, referring to different sources: data collected through my telescope, astrophotography, other scientists' research, and survey responses from over 40 people across 2 areas: Southern California and Guadalajara, Mexico. Through this, I have found strong correlations between increased aerosol particles from air pollutants and increased night sky brightness, increased aerosol particles amplifying cloud formation, which in turn increases light reflection, and a correlation between different areas and what they believe about pollution, with many being misinformed on air and light pollution themselves.
We investigate the relationship between synoptic/local meteorological patterns and PM10 air pollution levels in the metropolitan area of Naples, Italy. We found that severe air pollution crises occurred when the 850 and 500 hpa geopotential heights and their relative temperatures present maximum values above the city. The most relevant synoptic parameter was the 850 hPa geopotential height, which is located about 1500 m of altitude. We compared local meteorological conditions (specifically wind stress, rain amount and thermal inversion) against the urban air pollution levels from 2009 to 2013. We found several empirical criteria for forecasting high daily PM10 air pollution levels in Naples. Pollution crises occurred when (a) the wind stress was between 1 and 2 m/s, (b) the thermal inversion between two strategic locations was at least 3°C/200m and (c) it did not significantly rain for at least 7 days. Beside these meteorological conditions, severe pollution crises occurred also during festivals when fireworks and bonfires are lighted, and during anomalous breeze conditions and severe fire accidents. Finally, we propose a basic model to predict PM10 concentration levels from local
Air pollution remains a leading global health risk, exacerbated by rapid industrialization and urbanization, contributing significantly to morbidity and mortality rates. In this paper, we introduce AirCast, a novel multi-variable air pollution forecasting model, by combining weather and air quality variables. AirCast employs a multi-task head architecture that simultaneously forecasts atmospheric conditions and pollutant concentrations, improving its understanding of how weather patterns affect air quality. Predicting extreme pollution events is challenging due to their rare occurrence in historic data, resulting in a heavy-tailed distribution of pollution levels. To address this, we propose a novel Frequency-weighted Mean Absolute Error (fMAE) loss, adapted from the class-balanced loss for regression tasks. Informed from domain knowledge, we investigate the selection of key variables known to influence pollution levels. Additionally, we align existing weather and chemical datasets across spatial and temporal dimensions. AirCast's integrated approach, combining multi-task learning, frequency weighted loss and domain informed variable selection, enables more accurate pollution forec
Nowadays air pollution becomes one of the biggest world issues in both developing and developed countries. Helping individuals understand their air pollution exposure and health risks, the traditional way is to utilize data from static monitoring stations and estimate air pollution qualities in a large area by government agencies. Data from such sensing system is very sparse and cannot reflect real personal exposure. In recent years, several research groups have developed participatory air pollution sensing systems which use wearable or portable units coupled with smartphones to crowd-source urban air pollution data. These systems have shown remarkable improvement in spatial granularity over government-operated fixed monitoring systems. In this paper, we extend the paradigm to HazeDose system, which can personalize the individuals' air pollution exposure. Specifically, we combine the pollution concentrations obtained from an air pollution estimation system with the activity data from the individual's on-body activity monitors to estimate the personal inhalation dosage of air pollution. Users can visualize their personalized air pollution exposure information via a mobile applicatio
How did the Sun affect the air pollution on the Earth? There are few papers about this question. This work investigates the relationship between the air pollution and solar activity by using the geophysical and environmental data during the period of 2000-2016. It is quite certain that the solar activity may impact on the air pollution, but the relationship is very weak and indirect. The Pearson correlation, Spearman rank correlation, Kendalls rank correlation, and conditional probability were adopted to analyze the air pollution index (API), air quality index (AQI), sunspot number (SSN), radio flux at wavelength of 10.7 cm (F10.7), and total solar irradiance (TSI). The analysis implies that the correlation coefficient between API and SSN is weak ($-0.17<r<0.32$) with complex variation. The main results are: (1) For cities with higher air pollution, the probability of high API will be increased along with SSN, then reach to a maximum, and then decreased; (2) For cities with lower air pollution, the API has lower correlation with SSN; (3) The relationship between API and F10.7, or API and TSI are also similar as API and SSN. The solar activities take direct effect on TSI and t
Air pollution is a major global health hazard, with fine particulate matter (PM10) linked to severe respiratory and cardiovascular diseases. Hence, analyzing and clustering spatio-temporal air quality data is crucial for understanding pollution dynamics and guiding policy interventions. This work provides a review of Bayesian nonparametric clustering methods, with a particular focus on their application to spatio-temporal data, which are ubiquitous in environmental sciences. We first introduce key modeling approaches for point-referenced spatio-temporal data, highlighting their flexibility in capturing complex spatial and temporal dependencies. We then review recent advancements in Bayesian clustering, focusing on spatial product partition models, which incorporate spatial structure into the clustering process. We illustrate the proposed methods on PM10 monitoring data from Northern Italy, demonstrating their ability to identify meaningful pollution patterns. This review highlights the potential of Bayesian nonparametric methods for environmental risk assessment and offers insights into future research directions in spatio-temporal clustering for public health and environmental sci
Air pollution is a vital issue emerging from the uncontrolled utilization of traditional energy sources as far as developing countries are concerned. Hence, ingenious air pollution forecasting methods are indispensable to minimize the risk. To that end, this paper proposes an Internet of Things (IoT) enabled system for monitoring and controlling air pollution in the cloud computing environment. A method called Linear Regression and Multiclass Support Vector (LR-MSV) IoT-based Air Pollution Forecast is proposed to monitor the air quality data and the air quality index measurement to pave the way for controlling effectively. Extensive experiments carried out on the air quality data in the India dataset have revealed the outstanding performance of the proposed LR-MSV method when benchmarked with well-established state-of-the-art methods. The results obtained by the LR-MSV method witness a significant increase in air pollution forecasting accuracy by reducing the air pollution forecasting time and error rate compared with the results produced by the other state-of-the-art methods
Exposure assessment is fundamental to air pollution cohort studies. The objective is to predict air pollution exposures for study subjects at locations without data in order to optimize our ability to learn about health effects of air pollution. In addition to generating accurate predictions to minimize exposure measurement error, understanding the mechanism captured by the model is another crucial aspect that may not always be straightforward due to the complex nature of machine learning methods, as well as the lack of unifying notions of variable importance. This is further complicated in air pollution modeling by the presence of spatial correlation. We tackle these challenges in two datasets: sulfur (S) from regulatory United States national PM2.5 sub-species data and ultrafine particles (UFP) from a new Seattle-area traffic-related air pollution dataset. Our key contribution is a leave-one-out approach for variable importance that leads to interpretable and comparable measures for a broad class of models with separable mean and covariance components. We illustrate our approach with several spatial machine learning models, and it clearly highlights the difference in model mechan
Vehicular air pollution has created an ongoing air quality and public health crisis. Despite growing knowledge of racial injustice in exposure levels, less is known about the relationship between the production of and exposure to such pollution. This study assesses pollution burden by testing whether local populations' vehicular air pollution exposure is proportional to how much they drive. Through a Los Angeles, California case study we examine how this relates to race, ethnicity, and socioeconomic status -- and how these relationships vary across the region. We find that, all else equal, tracts whose residents drive less are exposed to more air pollution, as are tracts with a less-White population. Commuters from majority-White tracts disproportionately drive through non-White tracts, compared to the inverse. Decades of racially-motivated freeway infrastructure planning and residential segregation shape today's disparities in who produces vehicular air pollution and who is exposed to it, but opportunities exist for urban planning and transport policy to mitigate this injustice.
There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study relies on a flexible spatio-temporal prediction model that integrates land-use regression with kriging to account for spatial dependence in pollutant concentrations. Temporal variability is captured using temporal trends estimated via modified singular value decomposition and temporally varying spatial residuals. This model utilizes monitoring data from existing regulatory networks and supplementary MESA Air monitoring data to predict concentrations for individual cohort members. In general, spatio-temporal models are limited in their efficacy for large data sets due to computational intractability. We develop reduced-rank versions of the MESA Air spatio-temporal model. To do so, we apply low-rank kriging to account for spatial variation in the mean process and discuss the limitations of this approach. As an alternative, we represent spatial variation using thin plate regression splin
It is of utmost importance to have a clear understanding of the status of air pollution and to provide forecasts and insights about the air quality to the general public and researchers in environmental studies. Previous studies of spatio-temporal models showed that even a short-term exposure to high concentrations of atmospheric fine particulate matters can be hazardous to the health of ordinary people. In this study, we develop a spatio-temporal model with space-time interaction for air pollution data. The proposed model uses a parametric space-time interaction component along with the spatial and temporal components in the mean structure, and introduces a random-effects component specified in the form of zero-mean spatio-temporal processes. For application, we analyze the air pollution data (PM2.5) from 66 monitoring stations across Taiwan.
Air quality is a term used to describe the concentration levels of various pollutants in the air we breathe. The air quality, which is degrading rapidly across the globe, has been a source of great concern. Across the globe, governments are taking various measures to reduce air pollution. Bringing awareness about environmental pollution among the public plays a major role in controlling air pollution, as the programs proposed by governments require the support of the public. Though information on air quality is present on multiple portals such as the Central Pollution Control Board (CPCB), which provides Air Quality Index that could be accessed by the public. However, such portals are scarcely visited by the general public. Visualizing air quality in the location where an individual resides could help in bringing awareness among the public. This visualization could be rendered using Augmented Reality techniques. Considering the widespread usage of Android based mobile devices in India, and the importance of air quality visualization, we present AiR, as an Android based mobile application. AiR considers the air quality measured by CPCB, in a locality that is detected by the user's G
Air pollution is associated with human diseases and has been found to be related to premature mortality. In response, environmental policies have been adopted in many countries, to decrease anthropogenic air pollution for the improvement of long-term air quality, since most air pollutant sources are anthropogenic. However, air pollution fluctuations have been found to strongly depend on the weather dynamics. This raises a fundamental question: What are the significant atmospheric processes that affect the local daily variability of air pollution? For this purpose, we develop here a multi-layered network analysis to detect the interlinks between the geopotential height of the upper air (~5 km) and surface air pollution in both China and the USA. We find that Rossby waves significantly affect air pollution fluctuations through the development of cyclone and anticyclone systems, and further affect the local stability of the air and the winds. The significant impacts of Rossby waves on air pollution are found to underlie most of the daily fluctuations in air pollution. Thus, the impact of Rossby waves on human life is greater than previously assumed. The rapid warming of the Arctic cou