Air Pollution Prediction Models Research Articles

Deep learning-based air pollution analysis on carbon monoxide in Taiwan

Global air pollution poses a threat to humanity. Specifically, CO directly affects cardiovascular and other organ tissues and leads to numerous chronic diseases and major public health problems. The effective implementation of a deep learning model for predicting variations in CO levels would enable the early formulation of policies for controlling air pollution. In this study, a seasonal gated recurrent unit (SGRU) model, which is a deep learning time-series prediction model, was developed to predict the levels of CO in Taiwan. Atmospheric CO measurements from 2005 to 2021 were collected from the Environmental Protection Administration of Taiwan and preprocessed using the Kalman filter to achieve accurate forecasting. The performance of the proposed SGRU model was compared with that of the autoregressive integrated moving average (ARIMA), seasonal ARIMA, exponential smoothing (ETS), Holt–Winters ETS, support vector regression, and seasonal long short-term memory models in terms of mean absolute percentage error (MAPE) and root mean square error. The SGRU model achieved the lowest MAPE value of 0.94, which demonstrated its superior performance. The construction of an accurate air pollution prediction model can assist government entities in formulating health and social care strategies and in planning future air pollution control measures.

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R2) of 0.928, and root mean square error of 30.325 µg/m3. The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment.

Optimizing Long Short-Term Memory Network for Air Pollution Prediction Using a Novel Binary Chimp Optimization Algorithm

Elevated levels of fine particulate matter (PM2.5) in the atmosphere present substantial risks to human health and welfare. The accurate assessment of PM2.5 concentrations plays a pivotal role in facilitating prompt responses by pertinent regulatory bodies to mitigate air pollution. Additionally, it furnishes indispensable information for epidemiological studies concentrating on PM2.5 exposure. In recent years, predictive models based on deep learning (DL) have offered promise in improving the accuracy and efficiency of air quality forecasts when compared to other approaches. Long short-term memory (LSTM) networks have proven to be effective in time series forecasting tasks, including air pollution prediction. However, optimizing LSTM models for enhanced accuracy and efficiency remains an ongoing research area. In this paper, we propose a novel approach that integrates the novel binary chimp optimization algorithm (BChOA) with LSTM networks to optimize air pollution prediction models. The proposed BChOA, inspired by the social behavior of chimpanzees, provides a powerful optimization technique to fine-tune the LSTM architecture and optimize its parameters. The evaluation of the results is performed using cross-validation methods such as the coefficient of determination (R2), accuracy, the root mean square error (RMSE), and receiver operating characteristic (ROC) curve. Additionally, the performance of the BChOA-LSTM model is compared against eight DL architectures. Experimental evaluations using real-world air pollution data demonstrate the superior performance of the proposed BChOA-based LSTM model compared to traditional LSTM models and other optimization algorithms. The BChOA-LSTM model achieved the highest accuracy of 96.41% on the validation datasets, making it the most successful approach. The results show that the BChOA-LSTM architecture performs better than the other architectures in terms of the R2 convergence curve, RMSE, and accuracy.

Investigating the nonlinear and non-stationary relationship between PM2.5 and air pollutants by wavelet signal analysis in central Taiwan.

In recent years, PM2.5 has become a critical factor as an environmental indicator, causing severe air pollution that has negatively impacted nature and human health. This study used hourly data gathered in central Taiwan from 2015 to 2019 and applied spatiotemporal data analysis and wavelet analysis methods to investigate the cross-correlation between PM2.5 and other air pollutants. Furthermore, it explored the correlation differences between adjacent stations after excluding major environmental factors such as climate and terrain. Wavelet coherence shows that PM2.5 and air pollutants mostly have a significant correlation at the half-day and one-day frequencies, while the differences between PM2.5 and PM10 are only particle size; hence, not only is the correlation the most consistent among all air pollutants but also the lag time is the most negligible. Carbon monoxide (CO) is the primary source pollutant of PM2.5 as it is also significantly correlated with PM2.5 at most timescales. Sulfur dioxide (SO2) and nitrogen oxide (NOx) are related to the generation of secondary aerosols, which are important components of PM2.5; therefore, the consistency of significant correlations improves as the timescale increases and the lag time becomes amplified. The pollution source mechanism of ozone (O3) and PM2.5 is not identical, so the correlation is lower than for other air pollutants; the lag time is also obviously influenced by the season changes that have significant fluctuations. At stations near the ocean such as Xianxi station and Shulu station, PM2.5 and PM10 have a higher correlation in the 24-h frequency, while the SO2 and PM2.5 at Sanyi station and Fengyuan station, which are close to industrial areas, have significant correlations in the 24-h frequency. This study hopes to help better understand the impact mechanisms behind different pollutants, and thus construct a better reference for establishing a complete air pollution prediction model in the future.

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

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

Association of long-term exposure to air pollution with chronic sleep deprivation in South Korea: A community-level longitudinal study, 2008–2018

Background and objectiveAlthough there are many findings about the effects of fine particulate matter (PM2.5) and sleep deprivation on health respectively, the association between PM2.5 and chronic sleep deprivation has rarely been investigated. Thus, we aimed to investigate this association using a nationwide survey in South Korea. MethodWe examined the association between long-term exposure to PM2.5 and chronic sleep deprivation using a national cross-sectional health survey covering the entire 226 districts in inland South Korea from 2008 to 2018, with a machine learning-based national air pollution prediction model with 1 km2 spatial resolution. ResultsChronic sleep deprivation was positively associated with PM2.5 in the total population (odds ratio (OR): 1.09, 95% confidence interval (CI): 1.05–1.13) and sub-population (low, middle, high population density areas with OR: 1.127, 1.09, and 1.059, respectively). The association was consistently observed in both sexes (males with OR: 1.09, females with OR: 1.09)) and was more pronounced in the elderly population (OR: 1.12) than in the middle-aged (OR: 1.07) and young (OR: 1.09) populations. ConclusionsOur results are consistent with the hypothesis regarding the relationship between long-term PM2.5 exposure and chronic sleep deprivation, and the study provides quantitative evidence for public health interventions to improve air quality that can affect chronic sleep conditions.

Deep Learning and Machine Learning based Air Pollution Prediction Model for Smart Environment Design Planning

<p>For the past few decades, owing to human activities, urbanization, and industrialization, air pollution is becoming severe across several countries. Deep Learning (DL) and Machine Learning (ML) techniques had great contribution to the development of methods in various aspects of prediction, planning, and uncertainty analysis of smart cities and urban advancement in the current scenario. Many of the cities which are developed suffered from severe air quality (AQ) because of the rapid growth in industrialization and population. In this paper, we introduce a deep learning based air pollution prediction model for smart environment design planning (DLAPP-SEDP). The presented DLAPP-SEDP technique majorly intends to predict the level of air pollution in the smart environment. It follows a three-stage process namely data pre-processing, air pollution prediction, and hyperparameter tuning. At the initial stage, the presented DLAPP-SEDP technique performs various levels of data pre-processing such as missing value replacement, categorical value encoding, normalization, and feature selection. In the next stage, the DLAPP-SEDP technique employs graph convolutional network (GCN) model. Finally, the DLAPP-SEDP technique utilizes atomic orbital search optimization (AOSO) algorithm for optimal hyperparameter tuning process, showing the novelty of the work. To demonstrate the enhanced predictive efficiency of the DLAPP-SEDP method, a wide-ranging experimental analysis can be carried out. The experimental values assured the enhancements of the DLAPP-SEDP method over other recent techniques.</p>

Learning Adaptive Probabilistic Models for Uncertainty-Aware Air Pollution Prediction

The air pollution problem has been a serious issue for public health and city development in recent years, which rises an urgent demand for accurate air pollution prediction models. Traditional time series prediction scheme has been challenged in such case because the air pollution signals can be highly dynamic and uncertain. Distinct latent physical processes and complex environment changes over time make it hard for one fixed model to give consistently good performance. Also probabilistic prediction and uncertainty-aware estimation are critical when the model is used for public decisions in practice. However, few previous works can simultaneously provide adaptive and probabilistic air pollution prediction abilities, which are important practical issues for the real-world implementation of data-driven air pollution prediction models. In this paper we propose an adversarial meta learning approach to address these concerns. After reformulating the target signals as a collection of independent but similar mini-tasks along time, we adversarially learn a meta model that includes an implicit generator to adaptively give more informative probabilistic predictions across tasks. We further provide a bayesian meta learning interpretation that recasts the proposed model as an approximate minimizer for the Wasserstein distance between a generative latent model and the true data distribution. Our model provides an algorithm that explicitly models how the prediction distribution is conditioned on underlying data patterns and simultaneously gives adaptive uncertainty estimation. Experiments on both synthetic and real-world air pollution datasets show that the proposed model can simultaneously provide better probabilistic and adaptive prediction while keeping stratifying point prediction error.

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

Background and Aim: In recent years, there has been growing interest in developing air pollution prediction models to reduce exposure measurement error in epidemiologic studies. However, efforts for localized, fine-scale prediction models have been predominantly focused in the United States and Europe, with substantially less work conducted in low- and middle-income countries. Furthermore, the availability of new satellite instruments such as the TROPOspheric Monitoring Instrument (TROPOMI) provides novel opportunities for modeling efforts. Methods: We estimate daily ground-level nitrogen dioxide (NO₂) concentrations in the Mexico City Metropolitan Area at 1-km² grids from 2005 to 2019 using a multi-stage approach. In stage 1, we impute missing satellite NO₂ column measurements from the Ozone Monitoring Instrument (OMI) and TROPOMI using the random forest (RF) approach based on estimates from atmospheric ensemble models. In stage 2, we calibrate the association of column NO₂ to ground-level NO₂ using ground monitors and meteorological features using linear mixed-effects (LME) and RF models. In stage 3, we predict the stage 2 model over each 1-km² grid in our study area, then ensemble the results using a generalized additive model (GAM). In stage 4, we used RF to model the local component at the 200-m² scale by explaining the residual between predicted NO₂ from stage 3 and measured NO₂ at the monitoring stations. Results: The cross-validated R² of the LME and RF models in stage 2 were 0.69 and 0.75 respectively, and 0.83 for the ensembled GAM. Cross-validated root-mean-squared prediction error (RMSPE) of the GAM was 4.41 µg/m³. The RF model in stage 4 further explained on average 54% of the variation in the residual NO₂ concentrations. Conclusions: Using novel approaches and newly available remote sensing data, our multi-stage model presented high cross-validated fits and reconstructs fine-scale NO₂ estimates for further epidemiologic studies in Mexico City.

A Quantizing Method for Atmospheric Environment Impact Post-Assessment of Highways Based on Computational Fluid Dynamics Model

The post-assessment of highway atmospheric environmental impacts was limited by the traditional air pollution prediction model, which cannot adapt to complex terrain and complex obstacle scenes. The traditional model has a single evaluation index, which cannot accurately evaluate and predict the transient and long-term emissions of various pollutants. Based on the computational fluid dynamics model, this work establishes a post-assessment method of the atmospheric environment impact of the Beijing–Chengde Expressway construction project. The main pollution factors NOx and CO of highway traffic for transmission and diffusion simulation analysis were selected. The influence law of traffic function, environmental impact, meteorological conditions, and landform on the diffusion of pollution factors in complex tunnel sections were analyzed. It concludes that the pollution within 200 m along the expressway is severe and mainly concentrated in the tunnel entrance and gully area. The NOx concentration is generally higher than CO. The environmental quality is not up to standard and has a diffusion trend with increased traffic flow, operation time, wind speed, wind temperature, and wind direction frequency. The research results can provide theoretical guidance and technical support for the scientific post-assessment of highway environmental impact under complex conditions.

The Role of GARCH Effect on the Prediction of Air Pollution

Air pollution prediction is an important issue for regulators and practitioners in a sustainable era. Air pollution, especially PM2.5 resulting from industrialization, has fostered a wave of global weather migration and jeopardized human health in the past three decades. Taiwan has evolved as a highly developed economy and has a severe PM2.5 pollution problem. Thus, the control of PM2.5 is a critical issue for regulators, practitioners and academics. More recently, GA-SVM, an artificial-intelligence-based approach, has become a preferred prediction model, attributed to the advances in computer technology. However, hourly observation of PM2.5 concentration tends to present the GARCH effect. The objective of this study is to explore whether the integration of GA-SVM with the GARCH model can build a more accurate air pollution prediction model. The study adopts central Taiwan, the region with the worst level of PM2.5, as the source of observations. The empirical implementation of this study took a two-step approach; first, we examined the potential existence of the GARCH effect on the observed PM2.5 data. Second, we built a GA-SVM model integrated with the GARCH framework to predict the 8 h PM2.5 concentration of the sample region. The empirical results indicate that the prediction performance of our proposed alternative model outperformed the traditional SVM and GA-SVM models in terms of both MAPE and RMSE. The findings in this study provide evidence to support our expectation that adopting the SVM-based approach model for PM2.5 prediction is appropriate, and that prediction performance can be improved by integrating the GARCH model. Moreover, consistent with our prior expectation, the evidence further supports that taking the GARCH effect into account in the GA-SVM model significantly improves the accuracy of prediction. To the knowledge of the authors, this study is the first to attempt to integrate the GARCH effect into the GA-SVM model in the prediction of PM2.5. In summary, with regard to the development of sustainability for both regulators and practitioners, our results strongly encourage them to take the GARCH effect into consideration in air pollution prediction if a regression-based model is to be adopted. Furthermore, this study may shed light on the application of the GARCH model and SVM models in the air pollution prediction literature.

Air Pollution Prediction in Mass Rallies With a New Temporally-Weighted Sample-Based Multitask Learner

With the rapid development of industrialization, the environmental pollution issue is becoming increasingly serious, especially the air pollution problem. As the core of the prevention and control of air pollution, air pollution prediction plays a very significant role in human survival and development. Therefore, it is highly essential to develop an accurate air pollution prediction model for mass rallies (e.g., playground and bazaar). Recent studies have suggested that multiple air contaminants, e.g., PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> and PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> , which belong to a kind of aerosol, can carry the Covid-19 virus and spread it rapidly through the atmosphere, and this dramatically increases the risk of Covid-19 infection, particularly in the crowded and enclosed environment. Nevertheless, most existing air pollution prediction methods, which rely on large amounts of historical data for modeling and assume that the crowd flows relatively slow, are difficult to apply well to predict air pollution in mass rallies. To solve the aforementioned problem and better assist the decision-makers in managing environmental risk to human beings, in this article, we come up with a novel air pollution prediction model for mass rallies. More specifically, we first propose a temporally weighting matrix to differentiate the significance of training samples in the time domain. Then, we construct a temporal support vector regressor (TSVR), which puts more emphasis on the adjacent samples by considering the fact that the crowd usually flows promptly and disorderly in mass rallies. Finally, based on the extended TSVR, we develop a multitask TSVR (MTSVR) that simultaneously considers the related tasks. Since different air contaminants are correlated with each other, all the tasks can benefit by sharing information. The results of comparison experiments demonstrate that our presented MTSVR outperforms state-of-the-art single-task learners, multitask learners, and air pollution predictors when applied for air pollution prediction in mass rallies. Particularly, when under the six-task condition, the error values of the prediction of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> , PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> , and O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> obtained by our proposed method are relatively lower, outperforming the most advanced method tested by 15.2%, 6.1%, and 4.3%, and the precision values of the predicted values outperform the advanced method tested by 28.3%, 25.1%, and 24.8%.

Incorporation of Shipping Activity Data in Recurrent Neural Networks and Long Short-Term Memory Models to Improve Air Quality Predictions around Busan Port

Air pollution sources and the hazards of high particulate matter 2.5 (PM2.5) concentrations among air pollutants have been well documented. Shipping emissions have been identified as a source of air pollution; therefore, it is necessary to predict air pollutant concentrations to manage seaport air quality. However, air pollution prediction models rarely consider shipping emissions. Here, the PM2.5 concentrations of the Busan North and Busan New Ports were predicted using a recurrent neural network and long short-term memory model by employing the shipping activity data of Busan Port. In contrast to previous studies that employed only air quality and meteorological data as input data, our model considered shipping activity data as an emission source. The model was trained from 1 January 2019 to 31 January 2020 and predictions and verifications were performed from 1–28 February 2020. Verifications revealed an index of agreements (IOA) of 0.975 and 0.970 and root mean square errors of 4.88 and 5.87 µg/m3 for Busan North Port and Busan New Port, respectively. Regarding the results based on the activity data, a previous study reported an IOA of 0.62–0.84, with a higher predictive power of 0.970–0.975. Thus, the extended approach offers a useful strategy to prevent PM2.5 air pollutant-induced damage in seaports.

On the automated learning of air pollution prediction models from data collected by mobile sensor networks

ABSTRACT This paper addresses the problem of automated learning of air pollution predictive models that were trained using information gathered by a set of mobile low-cost sensors. Concretely, fast to compute machine learning methods (Decision Trees and Support Vector Machines) were used to build regression models that predict air pollution levels for a given location. The models were trained using the data collected by the OpenSense project, in particular, number of particulate matter, particle diameter, and lung deposited surface area (LDSA). We examined two different sets of attributes: one based on a geographical description of the location under analysis (e.g. distribution of households and roads), and another based on a time series of past air pollution observations in that location. Overall, we have found out that past measures lead to better pollution predictions. The best R2 score was 0.751 obtained with the model that predicts LDSA and was trained with the data set with time series attributes, while the worst R2 was 0.009 obtained with the geographical data set to predict number of particles. The performance of the best model is on par with similar air pollution systems. Moreover it can be used in a production system that requires frequent updates.

COLLABORATIVE AIR QUALITY MAPPING OF DIFFERENT METROPOLITAN CITIES OF INDIA

Abstract. Environmental pollution has become extremely serious as a result of today's technological advancements all over the world. One of the most important environmental and public health risks is air pollution. The exponential growth of population, vehicular density on highways, urbanization, and other factors are rising air pollution in cities, necessitating techniques for monitoring and forecasting air quality or determining its health consequences. Various experiments are being conducted on city air quality and its distribution through the built climate. The amount of emissions in the air varies according to the time of day as depicted it is merely high in morning time between 9 to 10 am and between 5 to 6 pm in all cities. These collected data are also characterized as peak hour, average hour, and off-peak hour. It also varies geographically and during special occasions. Since computing and showcasing of air pollution levels require terrain data, air quality data from the open sources i.e. CPCB (central pollution control board, India), and air pollution prediction models. Acculumating the data of the air pollution parameter from the open sources of cities based on typically very crowded, averagely crowded, and thinly crowded areas across the city and then mapping it on ArcGIS. The data monitoring has been done for the whole year merely main emphasizes has been done on the three seasons autumn, winter, and summer (January, May, and August). Also, in winter the value of having pollutants is high due to winter inversion and in the morning also the value is higher, and in monsoon, due to precipitation, it decreases. The dispersion model help in considering the wind speed and direction, the computed data from each source location reaching out to the monitoring sensing station from the comparatively adding to the value of pollutant. With the help of questionnaires, computed out to the result that people residing or having the workplace near to the busy crossing are more promising to have the health-related issue like chocking, respiratory diseases. Men are merely more affected by this between the age of 37 to 63 years.

An improved OIF Elman neural network based on CSO algorithm and its applications

In order to prevent air pollution and improve the living environment for residents, it is particularly important to carry out air quality forecasting. Air quality is affected by many factors, and showed significant non-linear features. Output–input feedback Elman (OIF Elman) neural network can effectively solve non-linear problems. However, the disadvantages of OIF Elman neural network are easy to fall into local minimum, slow convergence and inflexibility. Chicken swarm optimization (CSO) algorithm has high operating efficiency and fast convergence speed. Therefore, this paper proposes an air pollution prediction model for OIF Elman neural network based on the CSO algorithm (CSO-OIF Elman neural network model). Evaluation indicators are absolute average error and accuracy rate. The efficacy of the proposed model is compared with other models such as traditional Elman neural network model, OIF Elman neural network model and Elman neural network model based on CSO algorithm (CSO-Elman neural network model). The experimental results show that CSO-OIF Elman neural network model has the best accuracy and the smallest absolute average error value, and has higher nonlinear fitting capabilities and generalization capabilities. The establishment of this model can provide useful reference value for atmospheric prediction research.

Fine Particle Exposure and Clinical Aggravation in Neurodegenerative Diseases in New York State.

Background:Adult-onset neurodegenerative diseases affect millions and negatively impact health care systems worldwide. Evidence suggests that air pollution may contribute to aggravation of neurodegeneration, but studies have been limited.Objective:We examined the potential association between long-term exposure to particulate matter in aerodynamic diameter [fine particulate matter ()] and disease aggravation in Alzheimer’s (AD) and Parkinson’s (PD) diseases and amyotrophic lateral sclerosis (ALS), using first hospitalization as a surrogate of clinical aggravation.Methods:We used data from the New York Department of Health Statewide Planning and Research Cooperative System (SPARCS 2000–2014) to construct annual county counts of first hospitalizations with a diagnosis of AD, PD, or ALS (total, urbanicity-, sex-, and age-stratified). We used annual concentrations estimated by a prediction model at a resolution, which we aggregated to population-weighted county averages to assign exposure to cases based on county of residence. We used outcome-specific mixed quasi-Poisson models with county-specific random intercepts to estimate rate ratios (RRs) for a 1-y exposure. We allowed for nonlinear exposure–outcome relationships using penalized splines and accounted for potential confounders.Results:We found a positive nonlinear association that plateaued above (, 95% CI: 1.04, 1.14 for a increase from 8.1 to ). We also found a linear positive association (, 95% CI: 1.01, 1.09 per increase), and suggestive evidence of an association with AD. We found effect modification by age for PD and ALS with a stronger positive association in patients of age but found insufficient evidence of effect modification by sex or urbanization level for any of the outcomes.Conclusion:Our findings suggest that annual increase in county-level concentrations may contribute to clinical aggravation of PD and ALS. Importantly, the average annual concentration in our study was , below the current American national standards, suggesting the standards may not adequately protect the aging population. https://doi.org/10.1289/EHP7425

Long-term exposure to PM10 and NO2 in relation to lung function and imaging phenotypes in a COPD cohort

BackgroundAmbient air pollution can contribute to the development and exacerbation of COPD. However, the influence of air pollution on objective COPD phenotypes, especially from imaging, is not well studied. We investigated the influence of long-term exposure to air pollution on lung function and quantitative imaging measurements in a Korean cohort of participants with and without COPD diagnosis.MethodsStudy participants (N = 457 including 296 COPD cases) were obtained from the COPD in Dusty Areas (CODA) cohort. Annual average concentrations of particulate matter less than or equal to 10 μm in diameter (PM10) and nitrogen dioxide (NO2) were estimated at the participants’ residential addresses using a spatial air pollution prediction model. All the participants underwent volumetric computerized tomography (CT) and spirometry measurements and completed survey questionnaires. We examined the associations of PM10 and NO2 with FVC, FEV1, emphysema index, and wall area percent, using linear regression models adjusting for age, gender, education, smoking, height, weight, and COPD medication.ResultsThe age of study participants averaged 71.7 years. An interquartile range difference in annual PM10 exposure of 4.4 μg/m3 was associated with 0.13 L lower FVC (95% confidence interval (CI), − 0.22- -0.05, p = 0.003). Emphysema index (mean = 6.36) was higher by 1.13 (95% CI, 0.25–2.02, p = 0.012) and wall area percent (mean = 68.8) was higher by 1.04 (95% CI, 0.27–1.80, p = 0.008). Associations with imaging phenotypes were not observed with NO2.ConclusionsLong-term exposure to PM10 correlated with both lung function and COPD-relevant imaging phenotypes in a Korean cohort.

Hysteretic effects of meteorological conditions and their interactions on particulate matter in Chinese cities

Particulate matter (PM) has become the main air pollutant in most cities of in China, which greatly threatens the human health. Numerous studies have reported that PM concentrations could be accurately predicted from previous days’ meteorological conditions. However, the hysteretic or lagged effects of meteorological factors on PM concentrations are not clear. In this study, the GeoDetector q statistic method was used to quantify the hysteretic influences of meteorological conditions and their interactions on concentrations of PM2.5 (particles < 2.5 μm in aerodynamic diameter) and PM10 (particles < 10 μm in aerodynamic diameter) and the ratio of PM2.5 to PM10 (PM2.5/PM10) at multiple spatial and temporal scales in China. We found that the temperature, precipitation, and relative humidity had primary impacts on PM2.5 concentrations, PM10 concentrations, and the PM2.5/PM10 ratios, respectively, at both national and annual time scales. At the seasonal time scale, precipitation and relative humidity had hysteretic effects on PM concentrations in spring and summer, respectively. Moreover, precipitation and wind speed were dominant hysteretic factors in autumn and winter, respectively. At the regional scale, the dominant hysteretic effects of meteorological factors showed large variations across all regional subdivisions. The interactions between any two meteorological factors had an enhanced hysteretic effect on PM concentrations compared to a single factor. These findings could help us to develop scientific measures to reduce the emissions of air pollution and improve the performance of air pollution prediction models.

Spatial air quality index prediction model based on decomposition, adaptive boosting, and three-stage feature selection: A case study in China

Accurate prediction of air quality index (AQI) plays a vital role in pollution control and air quality warnings. Considering the strong spatial correlation of air pollutants, this study focuses on the spatial prediction of AQI and proposes a novel hybrid model combining decomposition, three-stage feature selection, and boosted extreme learning machine. The spatiotemporal features are gradually extracted in three stages, including spatial correlation analysis, mutuality, and binary grey wolf optimization (BGWO). Moreover, decomposition and ensemble methods are adopted to further enhance the robustness and accuracy of the model. Based on maximal overlap discrete wavelet packet transform (MODWPT), the original AQI series in target site is decomposed into eight subseries with different frequency bands to reduce the non-stationarity of time series. Outlier robust extreme learning machine (ORELM) serves as base predictor, and the adaptive boosting. MRT (Adabost.MRT) is adopted to combine several ORELMs to generate more robust forecasts. To comprehensively evaluate the proposed model, data collected from 1497 air quality monitoring sites in China are used, and six sites are randomly selected as prediction targets. The proposed model is compared with several baseline models and existing models. The results demonstrated that the proposed model significantly outperforms other models in all target sites. All components of the proposed hybrid model are proved to be indispensable to guarantee outstanding performance. The spatiotemporal data are reasonably taken into consideration, which provides improvement for air pollution prediction models.