A Data-Driven Approach to Air Pollution Management with Self-Tuning Deep Regression Neural Network

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.

In this study, the air quality index (AQI) of Indian cities of different tiers is predicted by using the vanilla recurrent neural network (RNN). AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo's derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. But, in this study, the basic vanilla RNN has been chosen to check the effectiveness of fractional derivatives. The AQI and gases affecting AQI prediction results for different cities show that the proposed algorithm leads to higher accuracy. It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM).

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.

Based on the history monitoring data of Air Quality Index (AQI) during 2015-2019, we conducted a contrastive analysis of the temporal and spatial variation characteristics of air quality in the Pearl River Delta. Results showed that the variation trends of AQI annual averages in the Pearl River Delta were basically the same. The AQI annual averages rose after decline and the variation curves were W-shaped. In the Pearl River Delta, Shenzhen and Huizhou enjoyed better air quality. In this region, AQI quarterly averages were high in autumn and winter but low in summer, and in a same year, the variation trends of the nine cities were basically the same. In autumn, the ozone concentration increased dramatically and therefore ozone became a major pollutant in ambient air. In winter, Guangzhou, Shenzhen and Dongguan saw a double-peak phenomenon in AQI hourly values, which rose at rush hours.

Obtaining comprehensive air quality information can help protect human health from air pollution. Existing spatially fine-grained estimation methods and forecasting methods have the following problems: 1) Only a part of data related to air quality is considered. 2) Features are defined and extracted artificially. 3) Due to the lack of training samples, they usually cannot achieve good generalization performance. Therefore, we propose a deep multi-task learning (MTL) based urban air quality index (AQI) modelling method (PANDA). On one hand, a variety of air quality-related urban big data (meteorology, traffic, factory air pollutant emission, point of interest (POI) distribution, road network distribution, etc.) are considered. Deep neural networks are used to learn the representations of these relevant spatial and sequential data, as well as to build the correlation between AQI and these representations. On the other hand, PANDA solves spatially fine-grained AQI level estimation task and AQI forecasting task jointly, which can leverage the commonalities and differences between these two tasks to improve generalization performance. We evaluate PANDA on the dataset of Hangzhou city. The experimental results show that our method can yield a better performance compared to the state-of-the-art methods.

Ambient air pollution is one of the big problems of today in perspective of environment and health degradation. The air quality of any place is measured in terms of the air quality index (AQI). The AQI of any place is decided by the highest value of one of the air pollutants out of the Particulate matter, Nitrogen dioxide, Ammonia, Sulphur Dioxide, Carbon Monoxide and Ozone of that place. Wavelet transform has become an important tool in the non-stationary and transient data/signal processing due to its time-frequency localization property. In wavelet transform as the scale increases, the resolution decreases, and a better estimate of the unknown trend of the data is obtained. We have selected Anand Vihar, Delhi, India as our study area and its daily average AQI data for one year before first lockdown (fromMarch 25, 2019 to March 24, 2020) and during lockdown (March 25, 2020 to June 08, 2020) is taken as the raw data.The spectral analysis of this AQI data is performed by using Haar wavelet, level-7 with help of MATLAB wavelet toolbox.The highest scale approximation i.e. trend of data and histogram of the data are shown and discussed.Some statistical parameter like average, kurtosis, skewness and standard deviation are also determined and discussed.

Urban air quality index forecasting using multivariate convolutional neural network based customized stacked long short-term memory model

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.

This research presents the MySecureMap system, a geolocation-based recommender system designed for real-time air quality visualization and safetyfocused navigation. The system leverages the Air Quality Index (AQI) data provided by both government and private sources. The proposed mechanism has integrated the Long Short-Term Memory (LSTM) neural network's machine learning technique which is capable of predicting the AQI levels and providing the route recommendations. The ultimate goal of the proposed system aims to protect users from the hazardous air pollutants by automatically identifying the risk zones and suggesting the safe routes which are visualized on the map. This paper discusses the proposed approach and the system architecture, the design and the development of the proposed MySecureMap system, and the performance evaluation, highlighting its usability and effectiveness in enhancing the environmental awareness and public health. The performance evaluation results shown that the proposed system met the acceptable accuracy with Mean absolute percent error (MAPE) of 2.39% when comparing the predicted forecast value with the real AQI data observed for the next 12 hours.

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.

AQI (Air Quality Index) is the standard degree that guides us to measure air pollution levels such as PM2.5, O3, NO2, and SO2 to show the state of air quality. Polluted gas causes much damage and problems to people, plants, and the environment because of its negative impact. Data mining successfully examines an enormous cluster of data to recognize associations, determine relations between variables, and predict future values. In this paper, an experimental study was performed on analyzing the previous dataset of (PM2.5 and O3) for accurately predicting AQI using deep learning Feedforward Neural network techniques. The deep learning (Feedforward Neural Network (FFNN) predicting models are employed to evaluate based on R, R², MSE, MAE, and RMSE criteria using historical data from (the Ministry of Environment-Oman). Different epochs and a different number of hidden layers are deployed to improve and boost performance. In FFNN, the epochs number increase by 50,100 and 500 while the hidden layer utilized to 1,5 and 10. This optimization technique exceeds the performance from R=0.892 to R=0.992 in predicting the level of (PM2.5) and the (O3) from R=0.864 to R=0.999. The results show that the Sohar Region in a safe level of AQI.

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.

In recent times, the global population surge has led to a parallel increase in air pollution levels, posing significant threats to the economy, environment, and public well-being. Traditionally, air pollution is measured by placing sensors on buildings spaced a certain distance apart. Nevertheless, there are disadvantages to this strategy, such as higher power consumption for sensor operation in each house and restricted application in remote areas with inadequate infrastructure. Using satellite data from FEMTOSAT for autonomous air pollution monitoring, analysis and mitigation emerges as a ground-breaking solution to these problems. This innovative approach circumvents the limitations associated with traditional methods, offering a more comprehensive and versatile solution. FEMTOSAT leverages the capabilities of LoRa (Long Range) communication, a crucial component in the Internet of Things (IoT), enabling data transmission over extended distances while consuming minimal power. LoRa communication is essential to the FEMTOSAT installation because it makes information sharing between the satellite and ground stations easier. Data is transmitted and received using radio frequency (RF) impulses, which make for a dependable and effective form of communication. The Air Quality Index (AQI) for a particular place is then determined using the environmental data that was gathered by the satellite. An important metric for measuring air quality, the AQI provides information about the degree of pollution or cleanliness in a particular place. The AQI is determined by a number of factors, including the concentrations of pollutants including NO2, CO, O3, PM2.5, SO2, and PM10. Making use of machine learning (ML) techniques such as Support Vector Machines (SVM), direct regression, time series analysis and logistic regression to forecast and analyse AQI trends makes it very efficient. One of the distinctive features of this study is the development of AQI mappings, which are derived from the comprehensive AQI data collected at specific locations. It has been shown via thorough investigation that ML-based AQI prediction models are more consistent and dependable overall. Accuracy and precision have been ensured in the data collection process through the simplified integration of new technology and smart sensors. Only machine learning algorithms can manage the complex analysis needed to produce safe and reliable predictions from large datasets in the field of environmental monitoring. The main objective of this effort is exemplified by the integration of Integrated Sensors as the payload in the FEMTOSAT mission. The benefits of the system—such as its affordability, lightweight construction, resilience, redundancy and low power consumption—highlight FEMTOSAT's applicability and effectiveness in handling the intricate problems related to air pollution monitoring.

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.

&lt;p&gt;Rapid economic development, industrialization, and urbanization in Indonesia have caused a large increase in air pollution with negative impacts on the environment and public health. The aim of this research is to use machine learning techniques to categorize air quality and generate an air quality index (AQI) using a dataset that includes six prevalent air pollutants. Next steps are preprocessing and data extraction, K-nearest neighbors (KNN) classification, support vector machine (SVM), and random forest (RF) models are implemented. Furthermore, synthetic minority oversampling technique (SMOTE) is incorporated into the ensemble learning process to improve the results. This research uses K-fold cross validation for improve classification accuracy and reduce overfitting. Research findings show that the application of SMOTE brings a significant increase in model accuracy, effectively solving the problem of imbalanced data sets. These insights provide direction for effective air quality monitoring systems and informed decision making in air pollution management.&lt;/p&gt;