Multi-criteria analysis for PM10 planning

The use of modeling to support environmental authorities to plan air quality control policies is now quite widespread in different parts of Europe. At the sub-national level the most common way to face the problem is through the scenario analysis approach, using Chemical Transport Models to assess the impact of emission reductions on pollution concentrations. In this paper a multi-objective approach to define air quality policies is proposed. The considered objective function is a vector including an Air Quality Index and an Internal Costs Index. In this work the Air Quality Index (AQI) is the yearly mean PM10 concentration over the study domain. It is estimated processing source-receptor models linking emission and concentrations over the domain cells, taking into account nonlinear phenomena and using Artificial Neural Networks (ANNs). The ExternE methodology is then applied to estimate health impacts and external costs of optimal control policies up to 2020. The data used to identify the source-receptor models has been provided by a set of 10 simulations computed through the TCAM (Transport Chemical Aerosol) model. The Internal Cost Index (CI) describes the costs to implement a particular emission reduction policy. This index is computed by means of ANNs linking emission reductions and their relative implementation costs, for different CORINAIR macro sectors. Simulations with IIASA’s GAINS model have been used to calibrate the emission-to-cost model. The solutions of the decision problem represent cost-effective policies at the sectoral level. The methodology is applied to Northern Italy, one of the most polluted areas in Europe, to select optimal control policies up to 2020.KeywordsIntegrated assessment modelsMulti-objective problem

Air pollution had become a vital concern for the 2014 Youth Olympic Games in Nanjing. In order to control air pollutant emissions and ensure better air quality during the Games, the Nanjing municipal government took a series of aggressive control measures to reduce pollutant emissions in Nanjing and its surrounding cities during the Youth Olympic Games. The Air Quality Index (AQI) is an index of air quality which is used to inform the public about levels of air pollution and associated health risks. In this study, we use the AQI and air pollutant concentrations data to evaluate the effectiveness of the implementation of control measures. The results suggest that the emission reduction measures significantly improved air quality in Nanjing. In August 2014, the mean concentrations of PM2.5, PM10, SO2, NO2, CO and O3 were 42.44 μg·m−3, 59.01 μg·m−3, 11.12 μg·m−3, 31.09 μg·m−3, 0.76 mg·m−3 and 38.39 μg·m−3, respectively, and fell by 35.92%, 36.75%, 20.40%, 15.05%, 8.54% and 47.15%, respectively, compared to the prophase mean before the emission reduction. After the emission reduction, the mean concentrations of PM2.5, PM10, SO2, NO2, and O3 increased by 20.81%, 41.84%, 22.84%, 21.16% and 60.93%, respectively, which is due to the cancellation of temporary atmospheric pollution control measures. The air pollutants diurnal variation curve during the emission reduction was lower than the other two periods, except for CO. In addition, the AQI of Nanjing and its surrounding cities showed a downward trend, compared with July 2014. The most of effective method to control air pollution is to implement the measures of regional cooperation and joint defense and control, and reduce local emissions during the polluted period, such as airborne dust, coal-burning, vehicle emissions, mobile sources and industrial production.

Background: Emerging evidence suggests that air pollution is a significant but under recognized contributor to cardiovascular disease (CVD) mortality, even in middle age adults. This study evaluates the association between long term statewise air quality, as measured by median Air Quality Index (AQI) and cardiovascular mortality rates among adults aged 35–44 years across the United States over a 25-year period. Methods: We linked annual statewise median AQI data from EPA AQS (2000–2024) with CDC WONDER mortality records for non-infectious CVD in the 35–44 age group. States were ranked by mean AQI to define “Highest 10” and “Lowest 10” AQI cohorts. Mean annual cardiovascular crude death rates were compared between these groups using t-tests, and trends were visualized year-by-year. Correlation and linear regression analyses assessed the association between AQI and mortality across all state years as well. Results: States with the highest long-term AQI (poor air quality) had significantly higher mean cardiovascular mortality rates (mean 46.99 per 100,000) than states with the lowest AQI (good air quality) (mean 30.35 per 100,000; p = 0.01). Yearwise analysis showed a persistent gap in mortality rates with the highest AQI states consistently exhibiting greater CVD mortality across the study period. Linear regression confirmed a significant positive association between AQI and mortality (slope = 0.62, p < 0.001), although AQI alone explained only a modest portion of the variation (R 2 = 0.095). Conclusions: Higher long term air pollution exposure, as measured by AQI, is associated with increased cardiovascular mortality among U.S. adults aged 35–44 years. This relationship is robust at the extremes of air quality and persists over time. This highlights the need for public health policies to improve air quality as a means to reduce early onset CVD mortality. Further studies should explore mechanisms and policy interventions to mitigate this risk.

This study investigates whether carbon trading improves air quality by using high-frequency daily data from China’s seven pilot carbon markets and real-time air pollution readings from 206 surrounding monitoring stations. We construct alternative indicators of carbon market liquidity and assess their effects on the Air Quality Index (AQI) using a fixed effects model. The findings show that poor liquidity—reflected by higher illiquidity ratios—is significantly associated with higher AQI values, suggesting that illiquid markets weaken incentives for carbon emission reductions and air quality benefits. Spatial heterogeneity suggests that pilot carbon markets with more efficient allocation generate greater air quality benefits. Temporal heterogeneity indicates that the effect is most pronounced in summer, moderate in winter and spring, and weakest in autumn. Furthermore, the observed improvement in air quality is primarily attributable to reductions in PM2.5. This study offers new evidence on the short-term environmental impacts of carbon trading and provide actionable insights for improving market design and regional policy alignment.

Air pollution has posed a serious public health issue in China. In the study, we aimed to examine the burden of air pollution and its association with climate factors and total mortality. City-level daily air quality index (AQI) data in 161 cities of China in 2014, and meteorological factors, socioeconomic status and total morality were obtained from China environmental, meteor-ology and healthcare agencies. Linear regression, spatial autocorrelation analysis and panel fixed models were applied in data analysis. Among 161 cities, monthly average AQI was significantly different by seasons and regions. The highest average AQI was in winter, and the lowest in summer. A significant clustering distribution of AQI by cities was observed, with the highest AQI in north China (22 cities, mean = 117.36). Among the 161 cities, 5 cities (3%) had AQI > 150 (e.g., moderate polluted reference value), and 50 cities (31.1%) had AQI between 100 and 150 (slightly polluted value). Heat index, precipitation and sunshine hours were negatively and significantly, but air pressure was positively correlated with AQI. Cities with higher AQI concentrations had higher total mortality than those with lower AQI. This AQI-mortality association remained significant after adjustment for socioeconomic status. In conclusion, the study highlights the burden and seasonal, regional and areas variations in air pollution across the nation. Air pollution is estimated to account for more than 4% of the urban health inequality in total mortality in China.

With the continuous progress of human production and life, air quality has become the focus of attention. In this paper, Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces were taken as the study area, where there are 58 air quality monitoring stations from which daily and monthly data are obtained. Firstly, the temporal characteristics of the air quality index (AQI) are explored. Then, the spatial distribution of the AQI is mapped by the inverse distance weighted (IDW) method, the ordinary kriging (OK) method and the Bayesian maximum entropy (BME) method. Additionally, cross-validation is utilized to evaluate the mapping results of these methods with two indexes: mean absolute error and root mean square interpolation error. Furthermore, the correlation analysis of meteorological factors, including precipitation anomaly percentage, precipitation, mean wind speed, average temperature, average water vapor pressure and average relative humidity, potentially affecting the AQI was carried out on both daily and monthly scales. In the study area and period, AQI shows a clear periodicity, although overall, it has a downward trend. The peak of AQI appeared in November, December and January. BME interpolation has a higher accuracy than OK. IDW has the maximum error. Overall, the AQI of winter (November), spring (February) is much worse than summer (May) and autumn (August). Additionally, the air quality has improved during the study period. The most polluted areas of air quality are concentrated in Beijing, the southern part of Tianjin, the central-southern part of Hebei, the central-northern part of Henan and the western part of Shandong. The average wind speed and average relative humidity have real correlation with AQI. The effect of meteorological factors such as wind, precipitation and humidity on AQI is putative to have temporal lag to different extents. AQI of cities with poor air quality will fluctuate greater than that of others when weather changes and has higher correlation with meteorological factors.

Air Pollution Index (API) is used in Malaysia to determine the daily air quality status, which is calculated based on the daily concentrations of particulate matter (PM10), ground-level ozone (O3), carbon monoxide (CO), sulphur dioxide (SO2) and nitrogen dioxide (NO2). This study presents short-term air quality trends based on API from the 52 air quality monitoring stations nationwide between 2010 and 2016. The air quality data and meteorological conditions were obtained from the Department of Environment and used for the API calculation. The API value is classified into six categories, namely: Good (0-50), Moderate (51-100), Unhealthy (101-200), Very Unhealthy (201-300), Hazardous (301-500), and Emergency (above 500). The coefficient of variation (CV) and Mann-Kendall trend test (MK) were used to assess the API variation and trend in each air quality monitoring station. Between the study periods, the API values were largely varied. Observation at 32 air quality monitoring stations have shown significant but small increasing trends, while 12 stations showed significant decreasing trends, and the remaining 8 stations showed no significant trends. The frequency of exceedance (API>50) was used to assess the percentages of unhealthy days. The analysis has found that air quality in Klang Valley was experiencing the highest number of unhealthy days, while the two Malaysian states in Borneo (Sabah and Sarawak) to be relatively less polluted.

Health is a primary concern for humans, and good air quality enables to live healthier. The Air Quality Index (AQI) was developed to better understand the air quality situation, which comprises parameters such as particulate matter and harmful gases. Currently several AQI monitoring systems are available to measure or forecast AQI. After a survey of existing system designs, two significant challenges were identified while developing an AQI monitoring system: first, making it low-cost, and second, achieving better efficiency in terms of experiencing lesser missing values. Addressing first challenge, two low-cost programming boards were used to sense and communicate AQI data over a cloud database; however, the setup experienced some delay in serial communication among the programming boards, resulting in missing values. To minimise missing values (the second challenge), low-cost AQI monitoring system design with a delay synchronization (LcDs) algorithm has been proposed to measure AQI values. The LcDs comprises a data acquisition unit to collect sensor data of AQI, a data processing and communication unit to calibrate and apply the proposed delay synchronization algorithm, and a data visualization unit to see the results in real-time. The algorithm synchronizes the communication between two programming boards and sends the data over the cloud data repository. On average, the efficiency of receiving correct sensor data was increased from 85.7% to 96.28% with the proposed delay synchronization algorithm. The proposed low-cost AQI setup design will further help disseminate AQI data from various locations in smart cities to monitor air quality and attain sustainability.

Rationale: Outdoor air pollution causes emergency department visits and hospitalizations for childhood asthma. In the United States, the Air Quality Index (AQI) alerts the public to air quality and provides behavioral recommendations to reduce exposure and harm, yet little is known about the relationship between the AQI and childhood asthma exacerbations. Objectives: To test for association between the AQI and childhood asthma exacerbations resulting in emergency department visits and hospitalizations. Methods: This was a retrospective time-stratified case-crossover study, conducted using medical records data from 2010 through 2018 for children aged 6-17 years with a primary diagnosis of an asthma exacerbation (defined as an emergency department visit or hospitalization for asthma) at UPMC Children's Hospital of Pittsburgh. Daily AQI data was obtained for Allegheny County, Pennsylvania from the Environmental Protection Agency. Conditional logistic regression was used for analyses of the AQI (as both a continuous and categorical variable) and asthma exacerbations. Stratified analyses were conducted to explore modification of the AQI effects on asthma exacerbations by race and other covariates. Results: There were 6,573 events. Particulate matter <2.5μm (PM2.5) was the primary pollutant responsible for the AQI, followed by ozone (62% and 29% of days with events, respectively). The overall AQI was associated with asthma exacerbations (e.g., as continuous, per 10-unit increase, LagDay2: odds ratio [OR], 1.014; 95% confidence interval [CI],1.003-1.025; Lag Day 3: OR, 1.012; 95% CI, 1.001-1.023). By pollutant-specific AQI, the association was strongest for PM2.5. In stratified analyses, the AQI was associated with exacerbations in Black and younger children (6-11 yr) on LagDay 4. Conclusions: The AQI is associated with asthma exacerbations among children in Allegheny County. This is driven primarily by PM2.5, with Black and younger children particularly affected. Healthcare providers should discuss the AQI in asthma management.

Forecasting of air quality in Delhi using principal component regression technique

An Air Quality Index (AQI) is a means of evaluating air quality in respect to the relation between air quality and the entire environment of an area. The main objective of the study was to compare the development of AQI using global methods and local methods to identify which could represent the more significant pattern from a spatial perspective in Dhaka city (DNCC and DSCC). The pollutants used in this study are CO, NO2, SO2, O3, CH4, CO2, PM2.5 and PM10. For the global AQI, indexing was performed using the guidelines in ‘Technical Assistance Document for the Reporting of Daily Air Quality – the Air Quality Index’ by USEPA (United States Environmental Protection Agency). This method identified and mapped the most responsible pollutant that represents the pollution pattern of the area. For local AQI, an overlay method was developed where all the relevant pollutants were weighed according to their significance and summed; their weights calculated using a Principal Component Analysis (PCA). Component analysis could find a better-correlated variable to represent the distribution from all the variables, deeming it a good feature-selecting tool. The result from this study suggests that the local AQI better represents the air quality of the study corresponding to certain real scenarios. Mapping the pollution has helped in validating the spatial pattern of the pollutants. Such methods, which include all the variables influencing the atmospheric dynamics, could establish a better pattern for the local environment. The temporal background of the factors could be considered as situations may change over time. For air quality, this could be performed with any variables apart from atmospheric constituents and can be even replicated for any component of the environment, which may help in comprehending the local environmental quality. The study would be useful to devise a proper system to develop AQI considering all the spatial and temporal effects. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 11(1), 2022: 131-149

Excellent air quality is crucial for human health, safety, security, and the environment. Air quality deteriorates as cities and businesses grow, affecting the existence of numerous species as well as the service life and aesthetic appeal of materials. Measuring and evaluating surrounding air quality is the first and most significant step in minimising air toxicity. A large number of water quality surveillance does not always inform the scientific community about the state of the air quality, policymakers, regulating bodies, and, most crucially, the general population. Environmental authorities utilize air quality indexes (AQI) to communicate and interpret information widely due to the health dangers connected with poor air quality. An air quality index is a unique number that represents the air’s quality in terms of its impact on human health. In its most sophisticated version, it combines multiple contaminant amounts in some mathematical equations to obtain at a single figure for air quality. The current study examines into how the COVID-19 pandemic spread affected the quality of the surrounding air during the lockdown and recovery phases. Changes in the air quality index were detected both during phases of the winter period, which lasted from October to January, using data from Adarsh Nagar-Central Pollution Control Board (CPCB) station. The findings found that the city’s quality of air had drastically worsened following the lockdown. When compared to the previous years’ data during the same era, before to the COVID-19 epidemic, the quantity of particulate and gaseous pollution increased dramatically following the lockdown period. It was noted that, in addition to PM 10, that was the primary pollutants in the air quality index previous to the epidemic, PM 2.5, and PM 10 were all present.This study presents a quick summary of the relatively significant areas that need more focus from lawmakers in order to launch policies targeted at creating adequate air pollution reduction measures.

Background and Objective: Air pollution is one of the current concerns. Human health has been faced with serious risks due to air pollution as a consequence of urbanization. Continuous monitoring of air quality to determine pollutants and identify their emissions sources are considered as the key strategies for air pollution control. The main objective of this study was evaluation of Mashhad air quality and determination of responsible pollutants in 2015. Materials and Methods: In this cross-sectional study, the instantaneous concentrations of air pollutants including O3, CO, SO2, NO2, PM2.5 and PM10 were measured at the three stations of Mashhad during 2015-2016. Based on Air Quality Index (AQI), Mashhad air quality was classified in groups of good, moderate, unhealthy for the sensitive people, unhealthy, very unhealthy and dangerous. Results: The results of the air quality index indicated that the air quality in 46 days was exceeded the standard limit in Mashhad in 2015-2016. The air quality category included 12.5 days unhealthy for sensitive groups, 74.5 days health. Conclusion: In this study, PM2.5 was determined as the main responsible pollutant in non-standard conditions of Mashhad air. Optimizing public transportation, considering environmental standards at the urban transportation management and Launch intelligent traffic control are the most appropriate strategies proposed to control Mashhad air pollution.

Air pollution is continuing to be at a high level in China, which may lead to an increased risk of obesity and hypertension. In the study, we aimed to examine the association between air pollution and prevalence of hypertension and tested whether this air pollution and hypertension association was mediated by changes in body weight. We used a linkage dataset from China with nationwide city-level air quality data linked with individual-level health data from the China Health and Retirement Longitudinal Study among adults aged 45 or older. Air quality was assessed based on the daily air quality index (AQI), a standard sum index of multiple air pollutants. To be specific, air pollution was measured by number of polluted days according to AQI and monthly average AQI. We examined the association between air quality and hypertension and tested mediation effect of body weight using a four-step mediating regression model. Air pollution was a risk factor for obesity. Each exposure to one more polluted day for females predicted on 2.1% (OR 1.021; 95% CI 1.007–1.035) higher prevalence of hypertension without BMI adjusted. A significant mediation effect of body mass index on the association between AQI and hypertension was observed. However, the effect of air pollution on hypertension among males was not significant. These results provide further evidence of the potential risks of air pollution and suggest that reducing pollution could help control both obesity and blood pressure.

Recently the intensity and magnitude of air pollutant concentration has grown up rapidly in the troposphere. The air pollution is primarily associated with automobiles and industrial sources Hyderabad, capital of Telangana, is a center of southern part of India for both industrial and commercial activity. It is one of the most populous urban areas with approximately 6.8 million inhabitants and over 73,000 small, medium and major industrial establishments sharing the greater city.Air pollution in India is monitored by the Central Pollution Control Board (CPCB) together with the State Pollution Control Boards (SPCBs) and the National Environmental Engineering Research Institute (NEERI) in Nagpur. The National Air Quality Monitoring Programme (NAMP) was started in 1984 with 7 sensor stations, 248 towns and cities have the air quality network of 591 Air Quality Monitoring Stations upto 2015, it is reported that in India 2022 added 180 manual air quality monitoring stations, increasing their number to 883 to achieve the target of 1,500 by 2024. For our study due to practical difficulties to collect, analysis the air quality, pollutants data has been collected from CPCB.The dataset contains City, Date, PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toulene, Xylene, Air Quality Index (AQI). Hyderabad was divided into 6 zones, air quality monitoring sensors is located at 14 places in and around six zones. For our study we selected 2 stations Newmalakpet and Sanathnagar based on heavy traffic area to analyse the maximum concentration of pollutants such as PM2.5, PM10, NO2, NO3, SO2, CO, for the period of three months ( Dec 2022,Jan, Feb 2023).It was observed at site 1 to site 52PM2.5,range 120 to 249 µg/m3 is very poor as per AQI, PM10 range 101.2 to 171 µg/m3 is moderately polluted as per AQI, NO2 range 7.46 to 32.9 µg/m3 is good as per AQI, NH3 range3.06 to 5.44 is good as per AQI, SO2 is 12.97 to 8.53 µg/m3 is good ,CO 61.83 to 71.9 is severe as per AQI, continuous air quality monitoring is necessary to protect environment and human health