Incorporating Spatio-Temporal Smoothness for Air Quality Inference

Since most of the existing air quality index (AQI) predicting models focused on prediction of the time series data of a single target monitoring station, they failed to consider the correlation and mutual influence among the air quality monitoring station sites and the spatio-temporal characteristics of air quality. And this will lead to a certain one-sidedness during air quality prediction of a particular site. Aimed at this problem, a short-term air quality prediction model based on K-nearest neighbor (KNN) and Long Short-Term Memory (LSTM) was proposed. The model firstly used KNN algorithm to select the time and space-related monitoring stations, then the air quality index sequences of these stations were constructed into data sets, followed by training and testing processes in the LSTM model, and finally the model was verified with real data. It is suggested that the prediction accuracy of the hybrid prediction model constructed in this paper is acceptable in terms of the space-time correlation, and it could be an alternative for further application of air quality prediction.

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.

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.

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.

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

The current study aims to characterize and estimate the particulate matter (PM) exposure level of the population near an opencast coal mine. PM concentrations of different sizes were monitored at 32 locations distributed uniformly in the study area for spatial profiling and at 1 location for analysing the temporal variations and characterization of dust. Health impacts of PM to nearby residents were estimated using the air quality index (AQI) values and were cross-validated using the health survey data. The monitoring results indicated that the 15 min average concentrations of PM10 and PM2.5 were 2.3–2.4 times of the corresponding 24 h National Ambient Air Quality Standards (NAAQS) in all the monitoring stations. Low wind speed during monitoring campaign that resulted in little PM dispersion is suggested to be the reason for this. AQI levels at five villages surrounding the mine were “moderate to severe” during morning-hours that improved to “satisfactory to moderate” during day-time. Eye irritation and respiratory problems were the two most adverse effects experienced by the residents living nearby to the mine. The health survey data and the corresponding AQI values indicate that respiratory problem is well correlated with the AQI values. The dust contains mainly six major elements in the order of O > C > F > Si > Al > Fe > Ca, accounting for 95% of the weight percentages of the 20 elements for which the characterization was carried out.

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.

Outdoor air pollution is a serious problem in many developing countries today. This study focuses on monitoring the dynamic changes of air quality effectively in large cities by analyzing the spatiotemporal trends in geo-targeted social media messages with comprehensive big data filtering procedures. We introduce a new social media analytic framework to (1) investigate the relationship between air pollution topics posted in Sina Weibo (Chinese Twitter) and the daily Air Quality Index (AQI) published by China’s Ministry of Environmental Protection; and (2) monitor the dynamics of air quality index by using social media messages. Correlation analysis was used to compare the connections between discussion trends in social media messages and the temporal changes in the AQI during 2012. We categorized relevant messages into three types, retweets, mobile app messages, and original individual messages finding that original individual messages had the highest correlation to the Air Quality Index. Based on this correlation analysis, individual messages were used to monitor the AQI in 2013. Our study indicates that the filtered social media messages are strongly correlated to the AQI and can be used to monitor the air quality dynamics to some extent.

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

BackgroundThe Air Quality Index (AQI) in the United States is widely used to communicate daily air quality information to the public. While use of the AQI has led to reported changes in individual behaviors, such behavior modifications will only mitigate adverse health effects if AQI values are indicative of public health risks. Few studies have assessed the capability of the AQI to accurately predict respiratory morbidity risks.Methods and findingsIn three major regions of California, Poisson generalized linear models were used to assess seasonal associations between 1,373,165 respiratory emergency department visits and short-term exposure to multiple metrics between 2012–2014, including: daily concentrations of NO2, O3, and PM2.5; the daily reported AQI; and a newly constructed health-based air quality index. AQI values were positively associated (average risk ratio = 1.03, 95% CI 1.02–1.04) during the cooler months of the year (November-February) in all three regions when the AQI was very highly correlated with PM2.5 (R2 ≥ 0.89). During the warm season (March-October) in the San Joaquin Valley region, neither AQI values nor the individual underlying air pollutants were associated with respiratory morbidity. Additionally, AQI values were not positively associated with respiratory morbidity in the Southern California region during the warm season, despite strong associations of the individual underlying air pollutants with respiratory morbidity; in contrast, health-based index values were observed to be significantly associated with respiratory morbidity as part of an applied policy analysis in this region, with a combined risk ratio of 1.02 (95% CI: 1.01–1.03).ConclusionsIn regions where individual air pollutants are associated with respiratory morbidity, and during seasons with relatively simple air mixtures, the AQI can effectively serve as a risk communication tool for respiratory health risks. However, the predictive ability of the AQI and any other index is contingent upon the monitored values being representative of actual population exposures. Other approaches, such as health-based indices, may be needed in order to effectively communicate health risks of air pollution in regions and seasons with more complex air mixtures.

We use the aerosol optical depth (AOD) measured by the moderate resolution imaging spectrometer (MODIS) onboard the Terra satellite, air pollution index (API) daily data measured by the Shanghai Environmental Monitoring Center (SEMC), and the ensemble empirical mode decomposition (EEMD) method to analyze the air quality variability in Shanghai in the recent decade. The results indicate that a trend with amplitude of 1.0 is a dominant component for the AOD variability in the recent decade. During the World Expo 2010, the average AOD level reduced 30% in comparison to the long-term trend. Two dominant annual components decreased 80% and 100%. This implies that the air quality in Shanghai was remarkably improved, and environmental initiatives and comprehensive actions for reducing air pollution are effective. AOD and API variability analysis results indicate that semi-annual and annual signals are dominant components implying that the monsoon weather is a dominant factor in modulating the AOD and API variability. The variability of AOD and API in selected districts located in both downtown and suburban areas shows similar trends; i.e., in 2000 the AOD began a monotonic increase, reached the maxima around 2006, then monotonically decreased to 2011 and from around 2006 the API started to decrease till 2011. This indicates that the air quality in the entire Shanghai area, whether urban or suburban areas, has remarkably been improved. The AOD improved degrees (IDS) in all the selected districts are (8.6±1.9)%, and API IDS are (9.2±7.1)%, ranging from a minimum value of 1.5% for Putuo District to a maximum value of 22% for Xuhui District.

The level of air quality at public transport stations: The Case of Torhailoch-Ayat main road in Addis Ababa