Air Quality Index Research Articles

A Cross-Regional Study on the Public's Channel Preferences for Accessing Information

In this study, an online questionnaire was administered simultaneously in Beijing and Seoul to investigate the public's channel preferences for air quality index (AQI) information and risk perception of air pollution in order to comparatively analyze the public's information acquisition behaviors in different media environments. As people become more aware of the health risks of air pollution, the AQI has become routine information released when there is a need to warn of health hazards, and many countries are releasing it to the public through a variety of information channels to ensure that the public can perceive the risks and take appropriate measures in a timely manner. This study, through a comparative cross-regional analysis, found that public preferences for accessing AQI information varied across regions with differing media environments. In addition, the correlation between air pollution risk perceptions and information access behaviors varied across regions.

Forecasting Daily Air Quality Index and Early Warning System for Estimating Ambient Air Pollution on Road Networks Using Gaussian Dispersion Model with Deep Learning Algorithm

The rapid growth of the vehicle population is a major factor in heavy air pollution and public health issues. Traffic-related air pollutants (TRAPs) on roads are often much higher than ambient values, leading to high exposure levels in vehicles. This research proposes a hybrid forecasting model for early detection and early warning systems (EWS) of road networks during real-world travels. Data is collected from Kannur, Calicut, Palakkad, and Coimbatore using real-time sensors, including surrounding discussion information, activity information, vehicle speed, and stopping events. The study predicts ambient air quality (AAQ) levels on the road network using the Gaussian Dispersion model (GDM) and measures the risk sensitivity of PM10 and PM2.5 in selected regions. This helps formulate powerful prevention strategies and prevent negative health impacts. The air pollution module for predicting concentration has an innovative hybridization model that combines an improved cuckoo search (CS) and differential evolution (DE) algorithm with a stacked LSTM model to increase forecasting accuracy of six major environmental pollution levels. This model predicts the AAQ level and is effective and robust for warning one day before the pollutant event occurs based on the risk level of an ambient air pollutant from the RN.

Cubic spline estimation for non parametric uncertain differential equation

. In the history of researching to estimate the unknown parameters that were in the uncertain differential equation (UDE), the problem of parameter estimation with known functional forms is often studied. However, in practical situations, its functional form is often unknown. In order to deal with this problem, this article proposes the cubic spline method to approximate the autonomous UDE, and perform non parametric estimation on it. The cross validation is introduced to determine the number of term (J), which is in the approximate cubic spline. In addition, the uncertain hypothesis testing is given to verify the rationality of this method. Finally, some numerical examples are given. Then this method is applied to a case study of the Beijing Air Quality Index, and a comparative analysis is given to verify the practicability and superiority of the cubic spline method.

Air quality, ES risks, and stock returns: Evidence from Korea

This paper examines the impact of air quality index (AQI) on stock returns. Using data from listed Korean firms for the 2015–2021 period, we show that the dispersion in AQI adversely affects stock returns with one-standard-deviation rise in AQI resulting in a 3.0 percentage-point decline in stock returns. This relationship however becomes less pronounced in firms with lower environmental and social (ES) risks and more institutional investments. Our results provide implications for investors and policymakers by highlighting the importance of managing ES risks and the role of institutional investments in mitigating the adverse impact of air pollution on stock returns.

Elemental Characteristics of Particulate Matter (PM₁₀ and PM₂.₅) from Peat Swamp Area in Kuala Pahang

Particulate matter (PM) is normally being divided into two common groups known as: the coarse fraction particle with a range of sizing from 2.5 to 10 UM (PM10–PM2.5), and the fine fraction particles with a dimension smaller than 2.5 µm (PM2.5). It is also can be defined as a sum of solid and liquid particles which suspended in the air. The contribution of particulate matter towards the air pollution from peat swamp area in Kuala Pahang can be generated from various sources such as unpaved road, vehicle, open burning and uncover soil. In fact, monsoon season also play an important role where PM can be conveyed by wind from one location to another. Air pollution has become a matter of concern over Malaysia. The objective of this survey is to determine the particular matters in the air by using Aeroqual AQM 65 which is located in Kuala Pahang, Pekan. Air Pollution Index (API) is the reading to determine the quality of air whether it suitable and safe for human being. The data is obtained from the Aeroqual AQM 65 machine starting for April 2019 till March 2020. The survey will last for 12 months to generate a reliable result. From the API gain, the government can work out with some ideal on improving the surrounding air quality in the meantime to generate different strategy to overcome the problems. According to Aeroqual AQM 65, the reading of concentration of PM10 and PM2.5 are almost the same yet the concentration of PM10 is still slightly higher than PM2.5. According to the analysis, there is a rapid increase of concentration of PM form June to September and a sharp drop from September to March due to the happening of monsoon season. When the direction of monsoon season changes, the graph tends drop because the wind tends to convey the PM away. Therefore, the reading of API of PM2.5 is higher than PM10 and the highest reading for both particular matter API is on September yet the lowest reading of API for both particular matters is on November.

Cost of escaping air pollution: A way to prevent excessive expansion of industrial areas

To pursue economic growth, many emerging markets prioritize providing land and resources to industries rather than people, resulting in people being forced to relocate. This paper uses Taiwan’s largest industrial city (Kaohsiung) as an example to discuss the negative spillover effect brought about by its most polluted industrial area (the Kaohsiung Linhai Industrial Park, KLIP). This study finds that the proximity between residences and the pollution sources and air pollution severity both cause housing prices to decrease. The results also show that among different air pollution indicators, SO2 concentration exerts the largest influence on housing prices. This may be because the SO2 emitted by large coal-fired power plants and oil refineries in the KLIP is linked to pollution-based haze and creates a visible atmospheric brown haze. By discussing the industrial park’s air pollution problem, this paper illustrates the harm of the overdevelopment of an industrial park. To prevent people’s relocation events resulting from the overdevelopment of an industrial park occurring in the future, we must regularly estimate the shadow price that residents living near the industrial park are required to pay to escape air pollution. This paper also puts forward suggestions that contribute to the sustainable development of industrial parks.

PM10 AIR QUALITY INDEX MODELING USING ARFIMA-GARCH METHOD: BUNDARAN HI AREA OF DKI JAKARTA PROVINCE

Air quality is an essential factor in urban life, and its’ assessment often relies on the concentration of measurable air pollution parameters. One critical parameter is Particulate Matter (PM), particularly PM10, which comprises solid or liquid particles dispersed in the air from various sources. One of the methods employed for predicting stock index prices is ARFIMA. ARFIMA is used to model long memory data characterized by a slowly decreasing Autocorrelation Function (ACF) plot (hyperbolic) or a difference value in the fractional from. This method is widely used due to its ability to handle nonstationarity issues in time series. However, the time series data often contain heteroskedasticity problems. Data with heteroscedasticity are then further addressed using the GARCH model, because it can model volatility changes occurring over longer periods and capture the persistence of volatility. The ARFIMA-GARCH model can explain long-memory patterns in time series data and address heteroscedasticity issues. The data are sourced from the Jakarta open data web, which is integrated with DLH DKI Jakarta Province. The aim of this research was to forecast the PM10 air quality index at the Bundaran HI Area in the Province of DKI Jakarta for the next 14 days, from January 1st to January 14th, 2021, using an ARFIMA model enhanced with GARCH. The analysis reveals that the best model is ARFIMA ([17], d, [1])-GARCH (1,1). Forecasting using this model resulted in a MAPE of 3.47%, indicating that the model is highly capable of forecasting several periods.

Fiscal decentralization and local environmental governance when performance evaluation Matters

ABSTRACT The impact of fiscal decentralization on social and economic development has been widely studied; however, there is extensive debate on whether fiscal decentralization can effectively promote local environmental governance. This article aimed to provide a new perspective by revealing the causality between fiscal decentralization and environmental governance. With the policy of disclosing the Air Quality Index (AQI) in China, we constructed a quasi-experimental framework and conducted an empirical analysis using a city dataset from 2007 to 2019 in China. The empirical results show that (1) whether fiscal decentralization contributes to local environmental governance depends on performance evaluation. (2) The governance effect is more pronounced in areas closer to air quality monitoring stations, regions that prioritized haze in their evaluation, and regions with more severe air pollution. The results of this article indicate that performance evaluation plays an important moderating role in the relationship between fiscal decentralization and environmental governance. Enhancing the level of decentralization and performance evaluation are effective strategies for enhancing the governance capacity of local governments.

A novel hybrid prediction model of air quality index based on variational modal decomposition and CEEMDAN-SE-GRU

As the economy rapidly develops, the phenomenon of air pollution is occurring more frequently, impacting on human health and environmental ecosystems. The Air Quality Index (AQI) is an essential index for describing air pollution levels. Accurate AQI prediction provides reliable information for air pollution control and protect human health. Therefore, in this paper, the AQI data of Beijing-Tianjin-Hebei region from 20 May 2014–18 November 2023 are used. We propose two basic prediction models based on Complete Ensemble Empirical Modal Decomposition with Adaptive Noise (CEEMDAN) and Gated Recursive Unit (GRU) as well as an improved prediction framework combining Variational Modal Decomposition (VMD). The best mean coefficient of determination (R²) is 0.984, the mean absolute error (MAE) is 2.476, and the R² is more than 0.980 for the prediction of AQI in different regions. These results validate the reliability of this hybrid prediction model.

A Study on Air Quality Analysis and the Impact of Pollutants and Climatic Factors

The study analyses how pollutants such as Particulate Matter, Sulphur dioxide, Nitrogen Oxides, Ozone, Benzene, Carbon Monoxide, Ammonia and weather conditions impact the Air Quality Index (AQI).Methods like Multiple Regression and Logistic Regression are used to examine the effects of pollutants (PM2.5, NO2, SO2) and weather factors (temperature, humidity) on AQI. The data type to be collected here will be secondary data from Kaggle.The sample size collected was 8000+, methodology used is descriptive research. The findings show that pollutants and climate factors significantly affect air quality, with recommendations for real-time monitoring, predictive modeling, and stricter regulations to improve air quality.

Evaluating the Prediction Performance of the WRF-CUACE Model in Xinjiang, China

Dust and air pollution events are increasingly occurring around the Taklimakan Desert in southern Xinjiang and in the urban areas of northern Xinjiang. Predicting such events is crucial for the advancement, growth, and prosperity of communities. This study evaluated a dust and air pollution forecasting system based on the Weather Research and Forecasting model coupled with the China Meteorological Administration Chemistry Environment (WRF-CUACE) model using ground and satellite observations. The results showed that the forecasting system accurately predicted the formation, development, and termination of dust events. It demonstrated good capability for predicting the evolution and spatial distribution of dust storms, although it overestimated dust intensity. Specifically, the correlation coefficient (R) between simulated and observed PM10 was up to 0.85 with a mean absolute error (MAE) of 721.36 µg·m−3 during dust storm periods. During air pollution events, the forecasting system displayed notable variations in predictive accuracy across various urban areas. The simulated trends of PM2.5 and the Air Quality Index (AQI) closely aligned with the actual observations in Ürümqi. The R for simulated and observed PM2.5 concentrations at 24 and 48 h intervals were 0.60 and 0.54, respectively, with MAEs of 28.92 µg·m−3 and 29.10 µg·m−3, respectively. The correlation coefficients for simulated and observed AQIs at 24 and 48 h intervals were 0.79 and 0.70, respectively, with MAEs of 24.21 and 27.56, respectively. The evolution of the simulated PM10 was consistent with observations despite relatively high concentrations. The simulated PM2.5 concentrations in Changji and Shihezi were notably lower than those observed, resulting in a lower AQI. For PM10, the simulation–observation error was relatively small; however, the trends were inconsistent. Future research should focus on optimizing model parameterization schemes and emission source data.

Statistical modeling of the effects of wind speed, air temperature and relative humidity on the concentration of carbon monoxide in the urban atmosphere

The high carbon monoxide content in the urban atmosphere is one of the most important indicators of poor air quality in megacities such as Moscow. This study is to evaluate the importance of wind speed, air temperature, and relative air humidity for predicting the concentrations of carbon monoxide for the day ahead using a simplified one-dimensional quasistationary statistical model. It is shown that the concentration of carbon monoxide in the Moscow atmosphere is determined by a combination of internal (previous days CO concentration) and external (meteorological conditions) factors. The variation of carbon monoxide concentration at one station differs from the variation at another station due to the differences in local conditions. Taking into account wind speed and air temperature increases the predictive value of the onedimensional quasi-stationary statistical model for most of the stations. In contrast to wind, relative air humidity decreases the predictive value of the model for most of the stations. This means that meteorological factors considered in this study could have different effects on predicting carbon monoxide concentration in the case of Moscow. The data from the Balchug weather station, located in the city center, offers a more accurate CO concentration forecast for most Moscow stations compared to the VDNKh weather station. For a more complete description of the influence of meteorological conditions on the predicted low concentration of gases, it is useful to take into account the model wind direction, surface air pressure, and the intensity of mixing in the urban boundary layer.

Developing a combined mapping representation of air and noise pollutions

Airparif and Bruitparif, observatories for air and noise pollutions in the Ile-de-France region, have worked together to develop a combined mapping representation of air and noise pollutions. On the one hand, Bruitparif has tested different approaches to propose a noise indicator taking into account the various exposures to road, air and traffic noise, according to the strategic noise maps. On the other hand, Airparif has developed an air quality indicator taking into account the main regulated air pollutants. They have finally put together an original method of mapping representation of the double exposure to noise and air pollutions. Mapping combining air and noise pollutions is an efficient diagnostic tool for municipalities. This makes it possible to identify the areas at stake: those that are preserved from these pollutions and should be protected, and those that are highly exposed and where action must be taken. It offers a wide range of uses, like cross-referencing of air-noise data with the location of establishments receiving vulnerable groups, or making comparison with socio-economic data and health indicators.

Hybrid FCMG-OP-FIS model approach to convert regression into classification data for machine learning-based AQI prediction

Air pollution from vehicle emissions, industrial activities, and medical facilities poses significant health risks in urban areas, underscoring the necessity for robust air quality index (AQI) monitoring. This paper presents a novel method for AQI prediction by integrating a fuzzy centre merge graph with an optimal value-based fuzzy inference system (FCMG-OP-FIS) and machine learning (ML). Traditional ML techniques encounter difficulties when converting regression datasets into classification formats, particularly when unable to label the dataset using the traditional method. The proposed FCMG-OP-FIS model efficiently converts regression data into a classification framework. Unlike traditional AQI prediction methods that rely solely on pollutant data, this approach incorporates both pollutant and meteorological data to improve prediction accuracy. The innovative fuzzy centre merge graph (FCMG) balances the dataset for optimal solutions and facilitates input grouping for Simulink, simplifying rule management. The FCMG-OP-FIS model generates a regression output for AQI, which is subsequently classified into levels (healthy, moderate, or unhealthy) using IF-THEN rules. To enhance accuracy further, a random forest classifier (RFC) is trained on the FCMG-OP-FIS classified output data. The regression output of the FCMG-OP-FIS model is validated using metrics such as RMSE (0.48), MSE (0.23), MAE (0.23), and MAPE (1.77%). Additionally, the classification output from the RFC model employs advanced validation techniques including stratified shuffle validation, grid search cross-validation, and confusion matrix analysis, achieving an accuracy rate of 99%, with the F1 score, precision, and recall over all at 99%. These results demonstrate the effectiveness of the proposed model in accurately labelling data for classification and predicting AQI through ML, highlighting its potential for practical application in environmental monitoring and management.

The effect of temperature and humidity of air on the concentration of particulate matter - PM2.5 and PM10

Particulate matters accepted as air quality markers are also indicators of the health risk of the population, especially children, because of their increased susceptibility to the quality of the air they breathe. Understanding the correlations between the concentration of particulate matter in the outdoor air and meteorological conditions will help to take steps to reduce pollution and reduce the risk of threats to health and life. For many years, scientists from all over the world have been looking for relationship between the impact of meteorological conditions on the concentration of particulate matter. Despite this, it is still not known whether the relationships will be similar in all locations. Furthermore, there is no agreement among scientists on the direction and strength of the correlation between temperature, humidity, and particulate matter concentrations. The article presents an attempt to link the impact of meteorological conditions on particulate matter concentrations depending on the classification of the area into air quality index in the particulate matter (AQIPM) category. Three locations with a specific air quality index were included: extremely poor, moderate & fair and good. The research was carried out at nine locations near educational facilities. Four measurement series were carried out for each location. Spearman's correlation coefficient was used to assess the relationship with an assumed significance level of 0.05. A research hypothesis was adopted: The correlation between particulate matter and meteorological conditions is different in locations with different AQIPM. The results obtained clearly show that the relationships between meteorological conditions and the concentration of particulate matter depend on the classification of the area into a specific category of the air quality index. The strongest correlation occurs in the fair & moderate AQIPM area. In relation to temperature, there is a negative correlation, and in relation to humidity, positive. In the extremely poor AQIPM area, no relationship was observed between humidity and particulate matter concentration. In the good AQIPM area, the correlation in relation to temperature was very weak, in relation to humidity, weak.

Air Quality Prediction using Ensemble Classifiers and Single Decision Tree

Air pollution is a major concern nowadays that needs immediate action. Various acts have already been initiated by the government in controlling it. The harmful gases present in the air are Nitrogen dioxide, Sulphur dioxide, Ozone, and Carbon monoxide that is causing air pollution and are becoming the major cause of harmful diseases. It does not affect human beings only but the entire environment. According to World Health Organization, air pollution is killing seven million people around the world. Planting trees and avoiding using plastics is the possible solution to control air pollution. The air quality index is the measure through which we categorize the air pollution of various cities. In this paper, we will discuss the bagging and boosting method, the extra trees method of machine learning in predicting air quality index, and its comparison with Decision trees. The dataset is collected from the Open Weather Application Program Interface. In this, the prediction of the Air Quality Index is done based on previous data. Time i.e., hourly prediction and space series prediction are done on the dataset. After implementing each algorithm confusion matrix is obtained and from this precision and recall are calculated for each category of the Air quality index ranging from one to five with one being good and five being very unhealthy. It has been observed that Gradient Boosting achieves the highest accuracy with 98.89%.

Enhanced O3 concentration due to biogenic emissions during a hot summer in 2018 over the Tokyo metropolitan area, Japan

Abstract Photochemical ozone (O3) is an important chemical component in the atmosphere as an indicator of air pollution. Anthropogenic sources, such as domestic automobiles and industries, have been considered a main source of O3 especially in urban areas; however, recent emission regulations have led to declining trends in anthropogenic sources. Therefore, other sources, such as biogenic emissions, could be important in the near future. In addition, increasing temperatures caused by global warming will relate to increasing biogenic emissions. In this study, the role of biogenic volatile organic compounds (BVOC) on O3 concentration was focused. The target period was a hot summer in 2018 in Japan, and O3 concentration by simulating with and without biogenic emissions over the Tokyo metropolitan area was analyzed. The result clarified that around 20% (up to 63%) of O3 was produced from biogenic sources at high temperatures, even in the Tokyo metropolitan area. This study suggested that the biogenic sources should be paid attention to and taken into account in the regulation of anthropogenic emissions.

Evaluating the Impact of Renewable Energy Integration on Air Quality: A Study of Pollutant Reduction in an Urban city of Calabar

The characterization of air quality parameters was carried out in the coastal city of Calabar with the aim of reducing air pollutants in the atmosphere. Both mobile and stationary measurements were obtained. Mobile data were used for estimating air quality index and creating air quality map. The results show that the average concentration of ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2) and nitrogen Oxides (NOx) was 0.34, 4.52, 0.53 and 0.96 ppm, respectively. The air quality index determined for each station showed that 82% of the stations were classified as “marginally polluted,” 14% were classified as “good,” and the remaining 4% were classified as “unhealthy” according to the U.S. air quality standards. Correlation analysis showed that wind speed had the highest correlation with SO2, R = -0.72, while temperature had a high correlation with ozone, R = -0.68. The 2016 polar plots show that CO sources are located in the south and southeast, NOx sources are located in the south and southwest, SO2 sources are located in the southwest, and O3 sources are located in the southeast. The 2017 polar plots show that CO sources are located in the northeast, NOx sources are located in the northwest, SO2 sources are located in the northeast, and O3 sources are located in the southwest.

Air quality historical correlation model based on time series

Air quality is closely linked to human health and social development, making accurate air quality prediction highly significant. The Air Quality Index (AQI) is inherently a time series. However, most previous studies have overlooked its temporal features and have not thoroughly explored the relationship between pollutant emissions and air quality. To address this issue, this study establishes a historical correlation model for air quality based on a time series model—the Gaussian Hidden Markov Model (GHMM)—using industrial exhaust emissions and historical air quality data. Firstly, a traversal method is used to select the optimal number of hidden states for the GHMM. To optimize the traditional GHMM and reduce error accumulation in the prediction process, the Multi-day Weighted Matching method and the Fixed Training Set Length method are utilized. Both direct and indirect prediction modes are then used to predict the AQI in the Zhangdian District. Experimental results indicate that the improved GHMM with the indirect mode provides higher accuracy and more stable state estimation results (MAE = 13.59, RMSE = 17.59, mean forecasted value = 117.94). Finally, the air quality historical correlation model is integrated with the air quality meteorological correlation model from a previous study, further improving prediction accuracy (MAE = 11.59, RMSE = 14.87, mean forecasted value = 120.88). This study demonstrates that the GHMM’s strong ability to analyze temporal features significantly enhances the accuracy and stability of air quality predictions. The integration of the air quality historical correlation model with the air quality meteorological correlation model from a previous study leverages the strengths of each sub-model in handling different feature groups, leading to even more accurate predictions.

EMD-based ultraviolet radiation prediction for sport events recommendation with environmental constraint

With the rising awareness of health and wellness, accurate ultraviolet (UV) radiation forecasts have become crucial for planning and conducting outdoor activities safely, particularly in the context of global sporting events arrangement and recommendation with definite constraint on environmental conditions. The dynamic nature of UV exposure, influenced by factors such as solar zenith angles, cloud cover, and atmospheric conditions, makes accurate UV radiation data forecasting difficult and challenging. To cope with these challenges, we present an innovative approach for predicting the UV radiation levels of a certain region during a certain time period using Empirical Mode Decomposition (EMD), a robust method for analyzing non-linear and non-stationary data. Our model is specifically designed for urban areas, where outdoor events are common, and integrates meteorological data with historical UV radiation records from specific geographic regions and time periods. The EMD-based model offers precise predictions of UV levels, essential for event organizers and city planners to make informed decisions regarding the scheduling, relocation and recommendation of events to minimize health risks associated with UV exposure. At last, the effectiveness of this model is validated through various experiments across different spatial and temporal contexts based on the Urban-Air dataset (recording 2,891,393 Air Quality Index data that cover four major Chinese cities), demonstrating its adaptability and accuracy under diverse environmental conditions.