Air Quality Index Research Articles

A novel hybrid features grey incidence model and its application in identifying key factors influencing air pollution in Jiangsu Province

Accurately assessing the key factors influencing air pollution is crucial for effective air pollution control. To address this need, we propose a novel Hybrid Features Grey Incidence Model (HFGIM), which integrates geometric feature differences from both proximity and similarity perspectives. Firstly, we extract geometric feature difference vectors of proximity and similarity from time series data and measure the overall feature difference degree by calculating vector norms. Secondly, we calculate the relative feature differences and information contribution rates of proximity and similarity to derive the hybrid feature differences coefficient between sequences, thereby obtaining the hybrid features incidence degree. After detailing the model’s properties and modelling steps, we introduce the Cross-sectional Data Hybrid Features Grey Incidence Model (C-HFGIM) and the Panel Data Hybrid Features Grey Incidence Model (P-HFGIM) for handling cross-sectional and panel data, respectively. Applying HFGIM, we identified the key pollutants and primary pollution source indicators of air pollution in Jiangsu Province. We also compared HFGIM with other classical grey incidence models to verify the proposed model’s effectiveness. Based on the analysis results, we propose policy recommendations for air pollution control in Jiangsu Province.

Unravelling key environmental factors influencing urban park visits: Thermal comfort and air quality

Urban parks play a vital role in shaping physical and mental health. Walking and cycling to local urban parks have become increasingly popular since the COVID-19 pandemic, whereas the climate change and air pollution continue to spur health risks for outdoor activities. Existing literature have focused on measuring the impact of temperature on park attendance, mainly using incomplete or short-term park visit data from field investigations. However, there is a lack of research on how thermal comfort factors (e.g., heat and humidity) and air quality indices – individually and synergistically – enhance or reduce visits to urban parks, particularly with quality official daily park visit data spanning an entire year. In response to the research gap, this study conducted a comparative case study of two urban parks in Shanghai, covering descriptive, correlation, and regression analyses of how weather and air quality mediate daily park attendance from April 1, 2020 to March 31, 2021. This study identifies that temperature, humidity, and the ‘Heat Index’ combining heat and humidity are strong parameters influencing the number of park visits, while the impacts of air quality indices are mostly trivial. Urban humid heat, a weather condition becoming more frequent with the rise of extreme heat events in Shanghai's recent summers, received much less attention than the sheer degree of temperature, threatening healthy outdoor activities in parks. These findings provide timely and important implications for better urban park planning, design, and management for promoting outdoor activities while reducing health risks.

The effect of the end-number license plate driving restriction on reducing air pollution in China

This study investigates the long-term efficiency of the long-run end-number license plate driving restriction in China, a traffic control policy that is partially aimed at reducing urban air pollution. A difference-in-differences regression is performed on a panel including nine cities that implemented this policy in staggered manner between 2008 and 2013. The results show that overall, this driving restriction does not improve air pollution in policy cities over the long-term. Quantitatively, the city-level air quality index has close-zero statistically insignificant changes by the policy, reaching only 3.9% reduction by 95% confidence interval lower bound of the estimate, translating to welfare gains of only 11.24 USD (70.62 CNY) per person per year, or 0.447 life years per capita. Further analysis with regression discontinuity in time and heterogeneity subgroup analysis illustrates that air pollution may first decrease due to the policy but then bounce back because of the behavioral adaptations of drivers purchasing a second car in cities without simultaneous car purchase restrictions. This shows that need to combine the end-number license plate policy with car purchase restrictions or electric vehicle promotions to achieve effective air pollution reductions over the long-term.

The temporal-spatial association of respiratory manifestations and air pollution in children referred to the Emergency Department of Akbar Children’s Hospital, Mashhad, Iran

BackgroundAir pollution causes many respiratory disorders, especially in children and the elderly. These disorders include asthma exacerbations, bronchiolitis, and pneumonia. Research on the association between air pollution and respiratory disorders helps to reevaluate environmental policies in developing countries.MethodsThis descriptive cross-sectional study was conducted on 932 children with respiratory manifestations admitted from December 2017 to December 2019 at the Emergency Department of Akbar Children’s Hospital of Mashhad University of Medical Sciences, Mashhad, Iran. Air pollution indices such as concentration of sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter (PM) smaller than 2.5 and 10 μm and other parameters, including the air quality index (AQI), air temperature, and humidity level from 2017 to 2019, were retrieved from Mashhad Environmental Pollution Monitoring Center. Demographic and clinical data of patients were collected from patients’ hospital documents. We used descriptive analytical methods such as central tendency, variability, and frequency distribution to report and analyze demographic and clinical data through tables and diagrams. The association between air pollution indices and respiratory manifestations was examined by the Spearman correlation test. The correlation between the AQI and total hospital admissions and asthma-related hospital admissions was also evaluated by the Spearman correlation test.ResultsHospital admissions due to respiratory manifestations were not associated with the AQI of each month (p-value = 0.794). The concentration of SO2 was correlated with respiratory-related hospital admissions (correlation coefficient = 0.487, p-value = 0.016) but not asthma attacks.ConclusionGenerally, our cross-sectional study showed no statistically considerable association between air pollution and hospital admissions due to respiratory manifestations and asthma attacks in children. Of the air pollution indices, only SO2 concentration was associated with respiratory-related hospital admissions but not asthma attacks.

ADNNet: Attention-based deep neural network for Air Quality Index prediction

The Air Quality Index (AQI) is a crucial indicator for assessing the degree of atmospheric pollution. Accurately forecasting AQI is notably challenging due to the unpredictable weather conditions and the intricate interactions among various pollutants. To this end, we propose a AQI prediction framework, entitled ADNNet, to develop a streamlined attention-based method for AQI prediction. Specifically, it includes a deep neural network architecture that exclusively integrates multi-layer perceptron (MLP) and attention mechanism modules. This approach involves a novel design paradigm, where data information is first fed into the MLP layer to learn global features, which are then refined by the attention module to capture local features such as time delays and periodicity. Additionally, we incorporate a Bayesian hyperparameter optimization algorithm to make the trade-off between computational time and prediction accuracy efficient. To further enhance model performance, we apply exponential smoothing (ETS) and its inverse, named inverse-ETS, boosting the model’s predictive accuracy. We leverage air pollution datasets from various cities to verify the effectiveness of the proposed ADNNet. The results demonstrate that the proposed method significantly outperforms state-of-the-art (SOTA) models like LSTM, N-BEATS, Informer, Autoformer and VMD-TCN. The compressed model with reducing the mean absolute error (MAE) by 4.17%–16.12%, decreasing the root mean squared error (RMSE) by 3.52%–12.72%, and reducing the mean absolute scaled error (MASE) by 5.26%–17.28%.The source code of our model is available at https://github.com/xiankuiwu/AQI-Attention-based-DNN.

Evaluation of Deep Learning Models for Predicting the Concentration of Air Pollutants in Urban Environments

Air pollution is an issue of great concern globally due to the risks to the health of humanity, animals, and ecosystems. On the one hand, air quality monitoring systems allow for determining the concentration level of air pollutants and health risks through an air quality index (AQI). On the other hand, accurate future predictions of air pollutant concentration levels can provide valuable information for data-driven decision-making to reduce health risks from short- and long-term exposure when indicators exceed permissible limits. In this paper, five deep learning architectures are evaluated to predict the concentration of particulate matter pollutants (in their fractions PM2.5 and PM10) and carbon monoxide (CO) in consecutive hours. The proposed prediction models are based on recurrent neural networks (RNNs), long short-term memory (LSTM), vanilla LSTM, Stacked LSTM, Bi-LSTM, and encoder–decoder LSTM networks. Moreover, a methodology is presented to guide the construction of the prediction model, encompassing raw data processing, model design and optimization, and neural network training, testing, and evaluation. The results underscore the precision and reliability of the Stacked LSTM model in predicting the hourly concentration level for PM2.5, with an RMSE of 3.4538 μg/m3. Similarly, the encoder–decoder LSTM model accurately predicts the concentration level for PM10 and CO, with an RMSE of 3.2606 μg/m3 and 2.1510 ppm, respectively. These evaluations, with their minimal differences in error metrics and coefficient of determination, validate the effectiveness and superiority of the deep learning models over other reference models, instilling confidence in their potential.

Forecasting ground-level ozone and fine particulate matter concentrations at Craiova city using a meta-hybrid deep learning model

Air quality forecasting is vital for managing and mitigating the adverse effects of air pollution on human health, crops, and the environment. This study aims to forecast daily time series of ozone and fine particulate matter (PM) concentrations using a meta-hybrid deep NARMAX (Nonlinear Auto-Regressive Moving Average with eXogenous inputs) model. Two datasets were utilised: (a) data on meteorological parameters (temperature, air pressure, relative humidity) and air pollutant concentrations (particulate matter, ozone, dioxide of carbon, volatile organic compounds, formaldehyde) provided by a sensor model A3 situated in the centre of Craiova city, and (b) data on wind direction, wind speed, and sunshine duration provided by the National Meteorological Administration. The data sets covered a time interval from December 10, 2020, to January 05, 2024. Initially, a statistical analysis was conducted to assess the correlation between variables. Results revealed that ozone concentration is primarily influenced by meteorological variables such as temperature (r = 0.79), sunshine duration (r = 0.55), and relative humidity (r = −0.48), and secondarily by air pollution indicators including VOC (r = −0.34), PM concentrations (r = −0.34), and CO2 (r = −0.3). In the subsequent stage, thirteen Machine Learning (ML) models were employed in conjunction with an integral feature selection (IFS) method to identify the best combinations of predictor variables for predicting ozone and PMs. Finally, a deep NARMAX model was developed to forecast the next periods of ozone and PMs based on the optimal combinations identified earlier. Results indicated the selection of sixty best models for ozone forecasting and four best models for PMs. The R2 values surpassed 0.97 for ozone and exceeded 0.8 for PMs, demonstrating the efficacy of the forecasting approach.

Evaluating the Environmental Indicators of Ambient Air Quality at a Carlisle Midsize Farm

Evaluating the Environmental Indicators of Ambient Air Quality at a Carlisle Midsize Farm

Application of a methodology for the development of air quality and health indices in Latin American countries

Application of a methodology for the development of air quality and health indices in Latin American countries

The association between the Daily Air Quality Index, inhaler prescriptions and mortality following Guy Fawkes Night celebrations in the UK

The association between the Daily Air Quality Index, inhaler prescriptions and mortality following Guy Fawkes Night celebrations in the UK

Comparing mechanical ventilation control strategies for indoor air quality: Monitoring and simulation results of a school building in northern Italy

Demand-controlled ventilation systems and carbon dioxide monitoring are critical to ensure indoor air quality (IAQ) comfort conditions. Their importance has become significantly more evident since the COVID-19 pandemic crisis. Control strategies for mechanical ventilation based on CO2 concentration are commonly used, although they are rarely applied in Italian and other Southern European country schools. Moreover, optimised ventilation systems can help maintain indoor air quality while reducing energy consumption for heating (i.e. exploiting heat recovery). The proposed paper aims to compare different remote-control strategies to manage three detached mechanical ventilation units (DMV) installed in a junior-high-school demo building in Torre Pellice (Northwest Italy), monitored with room detail since April 2021. Eight IAQ control logics are analysed considering both in situ tests, handling DMV accordingly to room sensors, and simulation tests using the Energy Management System of EnergyPlus in the neutral season. All different solutions are compared by considering air quality, temperature, and energy need indicators. Additionally, a yearly energy verification via hourly simplified analyses is performed by considering fan consumption and energy conservation. Threshold-based control logics result to be more effective than time-dependent ones, while all cases guarantee very high IAQ levels, although energy savings are limited. The choice of the correct control threshold(s) is site-dependent being influenced by user local habits.

Lichen diversity, substrate preference and environmental dynamics as indicators of air quality

Lichen diversity, substrate preference and environmental dynamics as indicators of air quality

A multi-task stations cooperative air quality prediction system for sustainable development

In recent years, A series of environmental problems caused by air pollution have attracted widespread attention. Air quality forecasting has become an indispensable part of people’s daily life. However, the traditional air quality prediction (AQP) model WRF-CMAQ simulation system faces several challenges: (1) the fuzziness of pollutant formation mechanism; (2) the hardness of integrating the features of meteorological conditions; (3) the difficulty of cooperating among monitoring stations. To deal with these challenges, we propose a multi-task station cooperative air quality prediction (MTSC-AQP) system for sustainable development. The MTSC-AQP system contains three modules: air quality analysis, a WRF-LSTM module for initial pollutant prediction, and multi-station cooperative AQP. Through air quality analysis, it can calculate the air quality index (AQI) and analyze the correlation between pollutants and meteorological conditions. Then, the WRF-LSTM module integrates spatio-temporal multi-source data to forecast the initial concentration of pollutants. Finally, the system incorporates the gravity model to predict the final AQI. Extensive experiments conducted on real-world datasets show the effectiveness of forecasting the AQI by using the MTSC-AQP system.

A novel hybrid optimization model for evaluating and forecasting air quality grades.

Air pollution has a significant global impact on natural resources and public health. Accurate prediction of air pollution is crucial for effective prevention and control measures. However, due to regional variations, different cities may have varying primary pollutants, posing new challenges for accurate prediction. In this paper, we propose a novel method called FP-RF, which integrates clustering algorithms to categorize multiple cities according to their air quality index values. Subsequently, we apply functional principal component analysis to extract the primary components of air pollution within each cluster. Furthermore, an enhanced random forest algorithm is utilized to predict air quality grades for each city. We conduct experimental evaluations using authentic historical data from Anhui Province spanning from 2018 to 2023. The results unequivocally establish the effectiveness of our model, with an average accuracy rate of 98.6% in forecasting six pollution grades and 96.04% accuracy in predicting 16 prefecture-level cities, surpassing performance compared to other baseline models.

Gaseous air quality and health risk assessment of high school kitchens in the Kumasi Metropolis

Cooking is a major factor that affects the quality of indoor air especially in kitchens. It is a daily activity essential for supplying the requisite energy and nutrients for living through food consumption. Exposure to air pollutants from cooking with solid fuels in domestic and commercial kitchens is a leading health risk in developing nations like Ghana. Real-time monitoring of gaseous pollutants (CO, SO2, NO2) was done during eight hours of occupational cooking in different cookstove kitchens of 14 Senior High Schools in the Kumasi Metropolis, Ghana. The traditional and improved cookstove kitchens were fueled by firewood whereas the briquette cookstove kitchens were fueled by palm kernel shell briquette. The observed median concentrations in the kitchens were 0.43–39.44 mg m−3 for CO, 0.07–0.36 mg m−3 for NO2, and 0.19–0.61 mg m−3 for SO2. The concentrations in the traditional and briquette cookstove kitchens exceeded the respective World Health Organization (WHO) thresholds of 4 mg m−3 for CO, 0.025 mg m−3 for NO2, and 0.04 mg m−3 for SO2 mg m−3. The air quality index of traditional cookstove kitchens was classified as hazardous for human occupancy. Hazard indices > 1 revealed the likelihood of significant non-carcinogenic health risks for cooks’ occupational exposure to gaseous pollutants in all the cookstove kitchens. This study is essential as it informs on gaseous air quality during large-scale cooking within institutional kitchens with different cookstove types in a developing country. The study fills a gap in literature by providing real-time concentrations of gaseous air pollutants to which cooks in high school kitchens of a developing country are occupationally exposed. The study recommends the urgent transition of large-scale kitchens in developing countries to cleaner energies and cookstove types, alongside regular monitoring and enforcement of air quality guidelines, to safeguard occupational health.

Determining Air Quality Influential Parameters Using Machine Learning Techniques

Air quality is an important issue in public health and the environment. This research aims to develop an air quality prediction model based on PM10 and PM2.5 parameters using various regression and machine learning approaches. The dataset used includes air pollutant standard index (ISPU) data from a number of stations in the Jakarta area with an observation period from January to April 2024. The research method includes collecting datasets, reviewing literature and testing several models of machine learning techniques. Furthermore, the handling of outliers was carried out using the numeric outliers node and data normalization to prepare the data before dividing the training and testing data. The models evaluated include Linear Regression, Random Forest Regression, Gradient Boosted Trees, and Multilayer Perceptron (MLP), with validation using 10 times cross-validation. The results showed that the Random Forest Regression and Gradient Boosted Trees models provided good prediction performance for both PM10 and PM2.5 parameters. Random Forest Regression showed the lowest RMSE value on testing data for PM10 (0.048) and PM2.5 (0.037), while Gradient Boosted Trees showed the lowest RMSE value on training data for PM2.5 (0.032). The process of handling outliers and normalizing the data successfully improved the prediction accuracy of the model. Suggestions for future research include the exploration of new models, the addition of meteorological and socio-economic variables, and the application of models in real-time air quality monitoring systems.

Using syndromic surveillance to rapidly assess the impact of a June 2023 wildfire smoke event on respiratory-related emergency department visits, Massachusetts, United States

Abstract A statewide air quality advisory was issued in Massachusetts for 6–7 June 2023 due to smoke originating from wildfires in Canada. Of particular concern was fine particulate matter, which has an aerodynamic diameter of ⩽2.5 μm (PM2.5) and has been linked to adverse respiratory outcomes. The objective of this study was to rapidly assess the impact of this wildfire smoke event on respiratory-related emergency department (ED) visits among Massachusetts residents. For exposure, daily air quality index (AQI) data from the US Environmental Protection Agency were used. Massachusetts counties, where for each day from 6 to 8 June 2023, the daily AQI was ⩾101 (i.e. unhealthy air quality), were considered exposed. For each exposed period, two unexposed reference periods where AQI < 101 (i.e. ‘good’ or ‘moderate’ air quality) were identified within the two weeks prior to the exposed period, with the same days of the week and in the same county. Data from the Massachusetts Department of Public Health’s syndromic surveillance system were used to examine daily counts of ED visits for asthma, air-quality-related respiratory illness, and all causes by county of residence, age group, race, and Hispanic/Latino ethnicity. For each outcome, the numbers of ED visits were compared between the exposed and reference periods. Overall, there were no large increases in ED visits for any conditions examined during this wildfire smoke event. However, residents who were aged 18–64 years, Hispanic/Latino or White experienced small but not statistically significant increases in asthma-related ED visits. These potential differences in the effect on asthma-related ED visits by age and race/ethnicity may be relevant for analyses of future events. This study provides an example of how real-time, publicly available exposure data can be used in conjunction with outcome data from syndromic surveillance to rapidly examine the impact of wildfires and other acute environmental events on health.

Optimized air quality management based on air quality index prediction and air pollutants identification in representative cities in China

With the ongoing challenge of air pollution posing serious health and environmental threats, particularly in rapidly industrializing regions, accurate forecasting and effective pollutant identification are crucial for enhancing public health and ecological stability. This study aimed to optimize air quality management through the prediction of the Air Quality Index (AQI) and identification of air pollutants. Our study spans nine representative cities (Hohhot, Yinchuan, Lanzhou, Beijing, Taiyuan, Xi’an, Shanghai, Nanjing, Wuhan) in China, with data collected from January 1, 2015, to November 30, 2021. We proposed a new model for daily AQI prediction, termed VMD-CSA-CNN-LSTM, which employed advanced machine learning techniques, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, and leveraged the chameleon swarm algorithm (CSA) for hyperparameter optimization, integrated through a variational mode decomposition approach. The model was developed using data from Lanzhou, with a split ratio of 8:1:1 into training, validation, and test sets, achieving an RMSE of 2.25, MAPE of 0.02, adjusted R-squared of 98.91%, and training efficiency of 5.31%. The model was further externally validated in the other eight cities, yielding comparable results, with an adjusted R-squared above 96%, MAPE below 0.1, and RMSE below 7.5. Additionally, we employed a random forest algorithm to identify the primary pollutants contributing to AQI levels. Our results indicated that PM2.5 was the most significant pollutant in Beijing, Taiyuan, and Xi’an, while PM10 was dominant in Hohhot, Yinchuan, and Lanzhou. In Shanghai, Nanjing, and Wuhan, both PM2.5 and PM10 were critical, with ozone also identified as a major air pollutant. This study not only advances the predictive accuracy of AQI models but also aids policymakers by providing a reliable tool for air quality management and strategic planning aimed at pollution reduction. The integration of these advanced computational techniques into environmental monitoring practices offers a promising avenue for enhancing air quality and mitigating pollution-related risks.

Spatial and temporal changes of air quality in Shandong Province from 2016 to 2022 and model prediction

This study investigates the spatial and temporal dynamics of air quality in Shandong Province from 2016 to 2022. The Air Quality Index (AQI) showed a seasonal pattern, with higher values in winter due to temperature inversions and heating emissions, and lower values in summer aided by favorable dispersion conditions. The AQI improved significantly, decreasing by approximately 39.4 % from 6.44 to 3.90. Coastal cities exhibited better air quality than inland areas, influenced by industrial activities and geographical features. For instance, Zibo's geography restricts pollutant dispersion, resulting in poor air quality. CO levels remained stable, while O3 increased seasonally due to photochemical reactions in summer, with correlation coefficients indicating a strong positive correlation with temperature (r = 0.65). Winter saw elevated NO2 levels linked to heating and vehicular emissions, with an observed increase in correlation with AQI (r = 0.78). PM2.5 and PM10 concentrations were higher in colder months due to heating and atmospheric dust, showing a significant decrease of 45 % and 40 %, respectively, over the study period. Predictive modeling forecasts continued air quality improvements, contingent on sustained policy enforcement and technological advancements. This approach provides a comprehensive framework for future air quality management and improvement.

Air Quality Prediction and Ranking Assessment Based on Bootstrap-XGBoost Algorithm and Ordinal Classification Models

Along with the rapid development of industries and the acceleration of urbanisation, the problem of air pollution is becoming more serious. Exploring the relevant factors affecting air quality and accurately predicting the air quality index are significant in improving the overall environmental quality and realising green economic development. Machine learning algorithms and statistical models have been widely used in air quality prediction and ranking assessment. In this paper, based on daily air quality data for the city of Xi’an, China, from 1 October 2022 to 30 September 2023, we construct support vector regression (SVR), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), random forests (RF), neural network (NN) and long short-term memory (LSTM) models to analyse the influence of the air quality index for Xi’an and to conduct comparative tests. The predicted values and 95% prediction intervals of the AQI for the next 15 days for Xi’an, China, are given based on the Bootstrap-XGBoost algorithm. Further, the ordinal logit regression and ordinal probit regression models are constructed to evaluate and accurately predict the AQI ranks of the data from 1 October 2023 to 15 October 2023 for Xi’an. Finally, this paper proposes some suggestions and policy measures based on the findings of this paper.