Air Quality Index Levels Research Articles

Enhancing Air Quality Index Activity Guidelines for Preventing Cardiovascular Events in the General Adult Population

BackgroundThe public health relevance of daily Air Quality Index (AQI) activity guidelines for the general adult public in the United States to prevent atherosclerotic cardiovascular disease (ASCVD) events is questionable. ObjectivesThe purpose of the study was to explore the utility of a policy tailoring activity guidance to calculated ASCVD risk rather than uniform recommendations to the general adult public as currently provided. MethodsWe calculated the number needed to treat (NNT) to prevent one ASCVD event per day by following activity recommendations across 10-year ASCVD risk scores (1% to 20%). Second, we modeled the benefits of tailoring recommendations to ASCVD risk. ResultsThe NNT decreased as ASCVD risk and/or AQI levels increased. At AQIs up to 151 (68% of days with AQIs above moderate in the United States), the NNTs remained untenably high (>2.7-55.3 million) across ASCVD risk. Under unhealthy conditions (AQIs 151-200), 28% of elevated AQIs, NNTs <1 million could be achieved by current guidance (15% exposure reduction), but only among the highest-risk individuals (ASCVD 18% to 20%) on the most polluted days (AQIs 192-200). Tailoring guidance to ASCVD risk could yield NNTs <1 million at risk thresholds of 7.5% and 10% if activity restrictions were more stringent (35% to 50% exposure reductions) during unhealthy conditions. ConclusionsASCVD risk has a major influence on the NNT to prevent cardiovascular events by following AQI guidance. It may be possible for a future policy to improve the utility of AQI activity guidance for the general adult public by tailoring activity recommendations to ASCVD risk.

Optimized ensemble deep random vector functional link with nature inspired algorithm and boruta feature selection: Multi-site intelligent model for air quality index forecasting

Air quality index (AQI) forecasting is complex due to its variability, instability, and inconsistent trends resulting from dynamic atmospheric conditions, various contaminants, and interactions between environmental factors. Advanced modeling techniques are needed to accurately forecast AQI values to capture subtle patterns and variations in air quality data. Thus, a new forecasting model is suggested in this study to improve the accuracy of AQI forecasting. The model integrates three-phase decomposition technique, a feature selection approach, and ensemble Deep Random Vector Functional Link (EDRVFL), optimized using adaptive teaching-learning-based optimization and differential evolution (ATLDE). The AQI series was first broken down into a group of intrinsic mode functions (IMFs) with different frequencies using multivariate variational mode decomposition (MVMD). Subsequently, a feature selection method based on the Boruta technique was applied to identify the most significant input variables. Finally, for daily AQI levels forecasting, ATLDE optimized the EDRVFL model (EDRVFL-ATLDE). Three daily AQI series gathered from Chengdu, Wuhan, and Taiyuan in China from January 1, 2018, to December 30, 2022, were used to test and confirm the proposed model via empirical research. Based on the results, the proposed model can yield the superior results for three cities (Chengdu: correlation coefficient (R = 0.987), root mean square error (RMSE = 5.583), Wuhan: (R = 0.987), (RMSE = 3.299), and Taiyuan: (R = 0.996), (RMSE = 4.521)) in China. The experimental findings demonstrated the feasibility of the three-phase hybrid methodology, outperforming all other models regarding forecast accuracy.

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.

Public Health Relevance of US EPA Air Quality Index Activity Recommendations

Reducing exposure to fine particulate matter (<2.5 μm [PM2.5]) air pollution improves cardiopulmonary morbidity and mortality. However, the public health relevance of air quality index (AQI) activity guidelines under present-day environmental conditions in the US has not been critically assessed. To evaluate the public health relevance of following PM2.5 AQI activity guidance in preventing serious atherosclerotic cardiovascular disease (ASCVD) and pulmonary events among adults in the US. This cross-sectional modeling study involved the general adult population and sensitive individuals as designated by the US Environmental Protection Agency (EPA), including adults with preexisting ASCVD or lung disease (asthma or chronic obstructive pulmonary disease). The study was conducted between August 1, 2023, and January 31, 2024. Daily AQI strata for PM2.5 and the corresponding activity recommendations. The main outcome was the number needed to treat (NNT) per day by following activity guidance across daily AQI strata to prevent 1 serious ASCVD or pulmonary event among relevant populations. To calculate PM2.5-induced excess disease event rates per day, estimated baseline disease-specific daily event rates for each group were multiplied by the increase in risks due to PM2.5 levels at each AQI stratum. The number of events prevented per day was calculated by multiplying each excess disease event rate by the percentage in exposure reduction plausibly incurred by following population-specific activity guidance at each AQI level. The NNT is the reciprocal of the number of events prevented. The NNT to prevent ASCVD events was high for the general population and for patients with ASCVD across all AQI strata. The range of values was comparatively lower to prevent pulmonary events among adults with lung disease. During most days (96%) when activity recommendations were promulgated due to elevated PM2.5 (AQI, 101-200), the NNT to prevent a serious disease event remained very high for the general population (>18 million), patients with ASCVD (approximately 1.6-5 million), and adults with lung disease (approximately 66 000-202 000). These findings suggest that existing PM2.5 AQI activity recommendations are of questionable public health relevance in present-day conditions and merit consideration for updating to improve their potential effectiveness.

Impact of Air Quality Index of Pune City: A Review

This work aims to perform a comprehensive analysis of earlier studies on Pune City's Air Quality Index (AQI). The main objectives are to define and analyze the current state of knowledge on Pune's air quality, point out significant study gaps, and provide details regarding upcoming studies and policies. According to the literature review, the quality of air in Pune City is declining due to its growing population. The primary air adulterants responsible for high AQI readings are particulate matter (‘PM-10 and PM-2.5’), nitrogen dioxide (‘NO2’), sulfur dioxide (‘SO2’), carbon monoxide (‘CO’), and ozone (‘O3’). Research reveals that industrialization, urbanization, and auto emigration all have an impact on air quality. Meteorological and seasonal conditions greatly impact the AQI readings in Pune. Research is necessary to determine the long-term health impacts of increased AQI levels, with more attention to sensitive groups like children, the elderly, and those with pre-existing medical issues. Furthermore, studies examining the efficacy of present regulatory measures and putting forth creative ways to improve air quality are essential. Pune City's air quality situation is thoroughly described in this literature review, which adds to the body of knowledge already in existence. A comprehensive approach and multidisciplinary cooperation are needed to tackle the intricate problems related to air quality in metropolitan settings such as Pune, as the synthesis of existing information makes clear. In the end, this assessment seeks to encourage additional investigation and action to lessen the negative impacts of air pollution on Pune City's environment and general public health.

Particulate matter pollution over artisanal crude oil refining areas of Niger-Delta Nigeria: Spatiotemporal analysis, transport modelling and risk assessment

The increasing rate of particulate matter pollution in Niger-Delta region of Nigeria due to artisanal crude oil refining and other unregulated emission activities is of great concern. This research work therefore quantify particulate matter (PM2.5) level over the Niger-Delta region of Nigeria. Also, backward trajectory and dispersion pattern of PM2.5 over six selected municipal areas within the region were simulated and health risks of PM2.5 exposure were assessed. IQAir satellite-based sensor was used to quantify PM2.5 (hourly) for 61 days (1st November 2022 to 31st December 2022) over the selected areas during the dreadful environmental and health challenged harmattan period. HYSPLIT model i.e Hybrid single-particle Lagrangian integrated trajectory model was used to simulate the trajectory, dispersion pattern and the dispered particulate matter position over the region. Air quality index (AQI) via the updated World Health Organization (WHO) standard was used to evaluate risks associted with PM2.5 exposure in the region. The result showed that the levels of PM2.5 quantified on daily average basis for the region were above the WHO standard. The trajectory models revealed the movement of the similar sourced PM2.5 pollutant and the dispersion model showed that it takes more than 24 hr for any released particulate matter to be fully dispersed. Also, majority of the dispersed particles were located between 0 m and 500 m. Finally, the AQI levels for the period considered ranged from unhealthy for sensitive groups (145.50 μg/m3) to hazardous (425.23 μg/m3) air quality. It can therefore be deduced that the inhabitants of the Niger-Delta region of Nigeria are liable to hazardous health effects over a prolong exposure to the high level PM2.5 in the region. Hence, more robust measures should be put in place to ensure an improved air quality and also to achieve a sustainable future for the Niger-Delta region, where economic growth can coexist with a healthy and pollution-free environment.

Bayesian Forecasting of Bounded Poisson Distributed Time Series.

This research models and forecasts bounded ordinal time series data that can appear in various contexts, such as air quality index (AQI) levels, economic situations, and credit ratings. This class of time series data is characterized by being bounded and exhibiting a concentration of large probabilities on a few categories, such as states 0 and 1. We propose using Bayesian methods for modeling and forecasting in zero-one-inflated bounded Poisson autoregressive (ZOBPAR) models, which are specifically designed to capture the dynamic changes in such ordinal time series data. We innovatively extend models to incorporate exogenous variables, marking a new direction in Bayesian inferences and forecasting. Simulation studies demonstrate that the proposed methods accurately estimate all unknown parameters, and the posterior means of parameter estimates are robustly close to the actual values as the sample size increases. In the empirical study we investigate three datasets of daily AQI levels from three stations in Taiwan and consider five competing models for the real examples. The results exhibit that the proposed method reasonably predicts the AQI levels in the testing period, especially for the Miaoli station.

Air quality index (AQI) changes and spatial variation in Bangladesh from 2014 to 2019

Introduction: The quality of air is becoming progressively worsen day to day. The increasing concentration of air pollution and its associated health effects are rapidly rising in Bangladesh and have drawn attention in recent years. The purpose of the research is to look into the increasing levels of air pollution in Bangladesh, specifically the proportion of Air Quality Index (AQI) in four seasons and six districts, monthly mean AQI, the correlation between PM2.5 and AQI between 2014 and 2019. Materials and methods: The AQI data from six monitoring stations have been collected for research purposes from Continuous Air Monitoring Stations (CAMS). MS Excel 2020 and IBM SPSS V27 were used for the analysis. Results: This study reveals that air quality in six districts of Bangladesh has been declining from 2014 to 2019, with winter and monsoon seasons being the most polluted. Dhaka and Narayanganj were found to have the unhealthiest (PM 2.5) air quality. There is a strong relationship between Paerticulate Matters (PM 2.5) and AQI, with AQI increasing with the amount of PM2.5 in all cities. In January, February, March, November, and December, the monthly mean AQI was higher, but in May, June, and July, the mean was lower. The F-values were significant based on seasons and stations. Overall, the study highlights the increasing air pollution and associated health effects in Bangladesh. Conclusion: Air pollution in Bangladesh is a significant issue due to industrialization, urbanization, transportation, and fuel use, resulting in consistently high AQI levels throughout the year except during rainy months.

Interannual variability and trends of summertime PM2.5-based air quality in the Intermountain West

Summertime air quality is a growing public health concern in the populated region of Northern Utah. Whereas winter air pollution is highly linked with local atmospheric temperature inversions associated with upper atmospheric high-pressure and radiational cooling in valleys, the relationship between climate factors and the frequency of poor air quality during summer is still unknown. Analyzing the last 20 years of data, we demonstrated that summertime unhealthy days (as defined by PM2.5 air quality index level) in Northern Utah highly correlate with the number of dry-hot days, wildfire size, and an upper atmospheric ridge over the Northwestern United States. The persistent atmospheric ridge enhances lightning-caused fire burned areas in northwestern states and then transports the wildfire smoke toward Northern Utah. Similarly, climate model simulations confirm observational findings, such as an increasing trend of the upper atmospheric ridge and summertime dry days in the northwestern states. Such metrics developed in this study could be used to establish longer-term monitoring and seasonal forecasting for air quality and its compounding factors, which is currently limited to forecasting products for only several days.

A Mobile Sensing Based Stochastic Model to Forecast AQI Variation of Pollution Hotspots on Urban Neighborhoods

Due to massive population migration, most Indian cities have experienced fast urbanization, resulting in a significant increase in construction activity, traffic pollution, and uncontrolled expansion. Some of these cities also have a high concentration of polluting industries, significantly worsening air quality. Pollution hotspots exist in certain cities, with levels well surpassing the authorized mark. Air pollution is generally classified as extremely hyper-local, which signifies that the pollution index decreases as we travel away from hotspots. Since the pollution data collected from traditional sources is occasionally inadequate, the extended consequences of such hotspots on neighboring communities remain unidentified. If the flux in pollution values in neighboring locales is efficiently mapped for locations encountered travelling further from identified hotspots, AQI levels for these areas can be forecasted and projected. Knowledge from monitoring these levels will aid the city administrations and government in drafting suitable proposals for susceptible establishments like hospitals and schools. In this research work, the Air Quality Index (AQI) data was accurately gathered at an identified pollution hotspot and its immediate neighborhood over a defined period along a specific route and a mathematical model was developed to forecast how AQI varies with distance for best results. Stochastic models such as ARMA and ARIMA were used to create the predicted model. Its reliability and performance were measured using various forecasting error calculation methods such as MPE (Mean Percentage Error), MAP (Mean Absolute Percentage), MAD (Mean Absolute Deviation), RMSE (Root Mean Square Error), and MSE (Mean Square Error).

Correlation Between Air Quality Index and Tear Film Lipid Layer Thickness: Comparison Between Patients with Sjogren’s Syndrome and with Meibomian Gland Dysfunction

Purpose To evaluate the effect of air pollution on the tear film stability by analyzing the correlation between Air Quality Index (AQI) and Lipid Layer thickness (LLT) in Xuzhou. Methods As a prospective descriptive observational study, 284 patients with meibomian gland dysfunction (MGD), 157 patients with Sjögren’s syndrome (SS), and 264 healthy volunteers were included. The tear film lipid layer thickness of the three groups of subjects was measured weekly and compared with the air quality index to analyze the correlation between the two indicators. Logistic regression analysis and linear regression analysis were used to analyze the effect of AQI on the thickness of the tear film lipid layer. The change of LLT with air pollution at different AQI levels was also analyzed. Results There are obvious seasonal differences in the changes of air pollution index in Xuzhou. Significant differences could be observed in the thickness of the lipid layer of the tear film among the three groups. LLT in the MGD group and SS group decreased with the aggravation of air pollution, while remained unchanged in the control group. There was strong evidence of correlation between LLT of the MGD group (F = 353.494, p < 0.01, adjusted R2 = 0.695) and the SS group (F = 502.404, p < 0.01, adjusted R2 = 0.764) with AQI, while there was minor correlation between LLT with AQI in control group (F = 8.525, p < 0.01, adjusted R2 = 0.046). Conclusions Air pollution can cause a decrease in the thickness of the tear film lipid layer, thereby affecting tear film stability, leading to the occurrence of dry eye.

High-resolution modeling for criteria air pollutants and the associated air quality index in a metropolitan city

The Air Quality Index (AQI), which jointly accounts for levels of criteria air pollutants relative to their guidelines, is largely reported at the city level. Little is known about the spatial patterns of the AQI in terms of the magnitude, temporal variability, and predominant air pollutant contributions at the hyperlocal scale within a city. To fill this research gap, we developed spatiotemporal models for each criteria air pollutant based on an advanced geostatistical framework and estimated daily AQI levels at 100-meter resolution in a metropolitan city in 2019. The model prediction ability (cross-validation, CV, Coefficient of determination, R2, and root mean square error, RMSE) ranged from 0.43 and 1.86 µg/m3 for sulfur dioxide (SO2) to 0.92 and 6.25 µg/m3 for fine particulate matter (PM2.5) across the six air pollutants, leading to good performance in the subsequent AQI estimations (CV R2 = 0.86, RMSE = 10.05). The AQI varies substantially over space at a fine scale and differs from the distributions of individual air pollutants. The unhealthy air quality (AQI > 100 over 75 days) spatial pattern was dominated by excessive ground-level ozone exposure in a large area. Our research provides a useful tool for accurately estimating AQI spatiotemporal variations for population health studies.

Monitoring the level of urinary metabolites of toluene and xylene in pregnant women in Isfahan and Khansar and their relationship with demographic factors and air quality index

Aim: In this study, the levels of urinary metabolites of toluene and xylene isomers in pregnant women in Isfahan and Khansar cities and their associations with air quality index (AQI) and demographic factors were investigated. Materials and Methods: Depending on the population, 55 pregnant women from Isfahan and 15 pregnant women from Khansar, a total of 70 people were enrolled in the study and 140 urine samples were collected during their 1st and 3rd trimesters of pregnancy (autumn and spring). Urinary metabolites of hippuric acid (HA), ortho-, meta-, and para-methyl HA (o-m-p-MHA) were extracted by dispersive liquid-liquid microextraction method, derivatized and analyzed using gas chromatography/mass spectrometer. The results were statistically correlated with AQI level and demographic factors using appropriate statistical methods. Results: Mean urinary concentrations of HA, o-m-p-MHA in the 1st trimester of pregnancy were 371.2, 1.14, 2.48, and 1.97 mg/g cr, and those in the 3rd trimester were 582.1, 3.54, 6.93, and 2.43 mg/g cr, respectively. In the 1st trimester, the mean urinary level of ortho-MHA was significantly associated with the outdoor exposure time on workdays and fragrances use (P < 0.05). In the 3rd trimester, the mean concentration of HA was significantly correlated to age and outdoor exposure time on workdays (P < 0.05). Conclusion: The mean urinary concentrations of HA and MHA isomers in the 1st trimester of pregnancy in Isfahan were more than those in Khansar. Because the level of air pollution in Isfahan was higher than Khansar and that in autumn was higher than spring. In the 3rd trimester of pregnancy, the mean levels of MHA isomers in Isfahan were more than those in Khansar. Only the amount of p-MHA was significantly associated with AQI in the 1st trimester of pregnancy. Thus, women who lived in high-traffic urban areas with high AQI were more exposed to toluene and xylene compounds.

A case study of air quality data of Ernakulam district

The main objective of an economist is to predict the future of any problem concerning the common man. Considering the human health in India, air pollution becomes a matter of major concern. The present study analyses the air quality data of Ernakulam district in Kerala by assessing various atmospheric pollutants and meteorological factors. The air pollution analysis of southern state, says that Ernakulam district has not been able to sustain the air quality gains of 2020 in the year 2021. As per 2021 air quality categorisation of analysis of days, it is seen that the number of days with worse air quality index has increased two times compared to previous year. The study comes to the conclusion that the meteorological factors have a great impact on the atmospheric pollutants and increasing humidity help to improve pollution. Also, the particulate matters are the major contributer for the increase in air quality index level. The most recent XLSTAT version and python programming were used to infer the results in this study. The researcher contends that, steps are to be taken to reduce the impact of solar radiations which helps in reducing the air quality level.

A Bidirectional Long Short-Term Neural Network Model to Predict Air Pollutant Concentrations: A Case Study of Tehran, Iran

Air pollution is a major challenge of new civilized world, which has intensified as a result of industrialization, urbanization expansion, and rapid traffic growth and increasing anthropogenic activities. Air pollutants can cause serious toxicological impacts on human health and the environment. Therefore, knowledge of pollutant concentrations can be used as key information in pollution control programs and policies. The main aim of this research was to provide a new hybrid model for short-term air quality prediction. Air pollution is the presence of pollutants in the air such as sulphur dioxide (SO2), particle substances (PM), nitrogen oxides (NOX), and ozone (O3). Sulfur dioxide (SO2), particulate matter with particle size less than 10 microns (PM10), particulate matter (PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) in the air all have an impact on the Air Quality Index (AQI). The AQI's variation is extremely nonlinear and complex. One of the most desired features of an AQI prediction model is the ability to predict the AQI level that is dangerous to the population. To increase forecast accuracy, an air quality prediction model based on deep recurrent neural networks and the BiLSTM model is created in this research. AQIs (O3, PM2.5, PM10, NO2, SO2, CO) prediction has been examined among several air pollutants. The data on O3, PM2.5, PM10, NO2, SO2, and CO concentrations are average daily reports from the Tarbiat Modares station in Tehran's 6th district, collected between March 2018 and June 2019. The station is located at 35.71751 latitude and 51.385909 longitudes. The proposed model was compared with Shallow Learning and MLP neural networks which had better and more reliable results over the aforementioned neural network in predicting the concentration of pollutants. The regression coefficient obtained to predict the concentrations of pollutants O3, PM2.5, PM10, NO2 and SO2 was 0.87, 0.62, 0.84, 0.67, 0.75 and 0.72, respectively. The results showed that the Bi-LSTM deep neural network model is a reliable method to predict hourly air pollutant concentrations.

Air Quality Classification and Measurement Based on Double Output Vision Transformer

The atmospheric quality monitor is an essential component of atmospheric protection. However, using complicated large-scale chemical equipment required by traditional methods is difficult for ordinary people. We are committed to allowing ordinary people to obtain a local air quality index (AQI) and then participate in the air quality monitoring of the entire district quickly and easily. So, we propose a method improved from Transformer in this article. This method processes pictures taken by mobile devices and predicts a more accurate local AQI level. The proposed method performs better than traditional methods in frequency and flexibility. Tokens in the Double Output Vision Transformer (DOViT) proposed in this article will be processed with the multihead self-attention (MSA) mechanism of the model to automatically extract the features in pictures for achieving higher classification accuracy. The application of Transformer makes our model more concise and efficient than previous CNN-based methods.

Acute cardiorespiratory response to air quality index in healthy young adults

BackgroundLittle is known about the acute health impacts of air quality index (AQI) on cardiorespiratory risk factors. ObjectivesTo assess the short-term links of AQI with cardiorespiratory risk factors in young healthy adults. MethodsWe performed a longitudinal panel study with 4 repeated visits in 40 healthy young adults in Hefei, Anhui Province, China from August to October 2021. Cardiorespiratory factors included systolic blood pressure (BP), diastolic BP (DBP), mean arterial pressure (MAP) and fractional exhaled nitric oxide (FeNO). We collected hourly AQI data from a nearby air quality monitoring site. Linear mixed-effects model was applied to assess the effects of AQI on BP and FeNO. ResultsThe study participants (75.0% females) provided 160 pairs of valid health measurements with average age of 24 years. The mean AQI level was 44.43 during the study period. There were significant positive associations of AQI with three BP parameters and FeNO at different lag periods. For example, an interquartile range increase in AQI (26.54 unit) over lag 0–24 h was associated with increments of 6.69 mmHg (95%CI: 2.95–10.44), 5.71 mmHg (95%CI: 3.30–8.13), 6.04 mmHg (95%CI: 3.46–8.62) and 5.67% (95%CI: 1.05%–16.05%) in SBP, DBP, MAP and FeNO, respectively. The results were robust after controlling for PM1. We did not find effect modifications by gender, BMI, physical activity, or AQI category level on the associations. ConclusionsThe current findings on associations of AQI with cardiorespiratory factors might add evidence of the acute adverse cardiorespiratory consequences following air pollution.

Spatiotemporal Regularity and Socioeconomic Drivers of the AQI in the Yangtze River Delta of China.

Air pollution has caused adverse effects on the climate, the ecological environment and human health, and it has become a major challenge facing the world today. The Yangtze River Delta (YRD) is the region with the most developed economy and the most concentrated population in China. Identifying and quantifying the spatiotemporal characteristics and impact mechanism of air quality in this region would help in formulating effective mitigation policies. Using annual data on the air quality index (AQI) of 39 cities in the YRD from 2015 to 2018, the spatiotemporal regularity of the AQI is meticulously uncovered. Furthermore, a geographically weighted regression (GWR) model is used to qualify the geographical heterogeneity of the effect of different socioeconomic variables on the AQI level. The empirical results show that (1) the urban agglomeration in the YRD presents an air pollution pattern of being low in the northwest and high in the southeast. The spatial correlation of the distribution of the AQI level is verified. The spatiotemporal regularity of the “high clustering club” and the “low clustering club” is obvious. (2) Different socioeconomic factors show obvious geographically heterogeneous effects on the AQI level. Among them, the impact intensity of transportation infrastructure is the largest, and the impact intensity of the openness level is the smallest. (3) The upgrading of the industrial structure improves the air quality status in the northwest more than it does in the southeast. The impact of transportation infrastructure on the air pollution of cities in Zhejiang Province is significantly higher than the impact on the air pollution of other cities. The air quality improvement brought by technological innovation decreases from north to south. With the expansion of urban size, there is a law according to which air quality first deteriorates and then improves. Finally, the government should promote the upgrading of key industries, reasonably control the scale of new construction land, and increase the cultivation of local green innovative enterprises.

Discussion of Air Quality Forecasting Evaluation for Cities Based on Half-level Method

Ambient air quality forecasting evaluation plays an important role in improving forecasting capability. In order to provide better support for refined air quality management, with reference to the UK air quality forecasting evaluation method, this study divided six air quality index (AQI) levels into 12 half-levels and explored AQI, PM2.5, and O3-8h concentration forecasting evaluation based on the half-level method in "2+26" cities during 2020. Comparison with the AQI range forecasting and AQI level range forecasting evaluation showed that the half-level forecasting evaluation method could combine these two indicators into one, providing feasibility and application value in operational air quality forecasting evaluation. Specific half-level forecasting evaluation showed that the forecasting effect of AQI and O3-8h concentration at the low and high levels was significantly worse than that of the middle levels in "2+26" cities. The forecasting effect of the PM2.5 concentration was relatively stable in different half-levels. The monthly variation curves of AQI, PM2.5, and O3-8h concentration forecasting accuracy exhibited a bimodal pattern, first rising and then falling and a flat pattern, respectively. The overestimate of PM2.5 concentration forecasting was significant in each month. The accuracy gaps of AQI and O3-8h concentration forecasting in different cities was relatively small; however, the PM2.5 concentration forecasting accuracy fluctuated greatly. The AQI forecasting accuracies of Beijing and Tianjin were higher than that of neighboring provinces. Additionally, the PM2.5 and O3-8h concentration forecasting effects in Beijing and Henan province were relatively the best.

A survey on the correlation between PM2.5 concentration and the incidence of suspected and positive cases of COVID-19 referred to medical centers: A case study of Tehran

COVID-19, one of the greatest health challenges of the present century, has infected millions of people and caused more than 6 million deaths worldwide. The causative agent of this disease is the new virus SARS-CoV-2; which continues to spread globally and sometimes with new and more complex aspects than before. The present study is an observational study aimed to investigate the role of AQI; PM2.5 and its relationship with the incidence of suspected cases (SC) and positive cases (PC) of COVID-19 at different levels of the air quality index (AQI) in Tehran, the capital of Iran in the period from Feb 20th, 2020 to Feb 22nd, 2021. Data on AQI were collected online from the air monitoring website of Air Quality Control Company under the supervision of Tehran Municipality. The data on suspected and positive cases were obtained from the Iranian Ministry of Health. The results and statistical analysis (Pearson correlation test) showed that with the increase of AQI level, the number of suspected cases (SC) and positive cases (PC), also increased (P-value<0.01). The average daily number of suspected and positive COVID-19 cases referred to medical centers, at different levels of the AQI was as follows: level II: yellow, moderate (SC: Nave = 466; PC: Nave = 223), level III: orange, unhealthy for sensitive groups (SC: Nave = 564; PC: Nave = 275), and Level IV: red, unhealthy (SC: Nave = 558; PC: Nave = 294). The results of the GEE for seasonal comparison (winter as reference season), showed that there is an epidemiological pattern in autumn with colder weather compared to other seasons in both suspected (Cl: %95, B = 408.94) and positive (Cl: %95, B = 83.42) cases of COVID-19. The results of this study will serve policymakers as an informative tool for guidance on the importance of the role of air pollution in viral epidemics.