Visibility Graph Network Analysis of Air Quality Data

Dhaka city has experienced heat waves and extreme weather events in recent years, leading to elevated levels of air pollution primarily attributed to vehicle emissions, industrial activities, and domestic burning. The World Health Organization has dubbed air pollution as the "silent killer," contributing to a global threat with 262.41 million asthma cases worldwide. Study has confirmed a strong correlation between asthma and both outdoor and indoor air pollutants. Despite the unknown impact of macro-environmental factors on asthma burden in Bangladesh, this study sheds light on the connection between indoor and outdoor air pollution, climate change, and respiratory health in Dhaka. It quantifies their influence on hospital admissions, highlighting the need for proactive measures to address these challenges. The research aims to investigate the influence of air pollution and climate change on asthma hospital admissions in Dhaka City over the past five years. This involves integrating ground measurements, satellite data (Sentinel 5p & CAMS), and community-level asthma patient data. The study unveils a significant relationship between climate change, air pollution, and asthma-related health issues in Bangladesh. Utilizing a Generalized Additive Modelling (GAM), the analysis of climate and air quality data from urban locations and various hospitals in Dhaka reveals a substantial association. The study explores the correlation between asthma-related hospital admissions and air quality parameters (NO2, CO, SO2, PM2.5, PM10, O3) as linear variables, with temperature, relative humidity, and wind speed as covariates. The findings from the Generalized Additive Modeling using ground data indicate that increased levels of PM2.5, SO2, PM10, O3, CO, and NO2 in Dhaka City are linked to progressively heightened risks of asthma hospital admissions. Immediate spikes in PM2.5, SO2, CO, and NO2 result in a higher relative risk of asthma prevalence, leading to an increased number of hospital admissions. In contrast, PM10 shows an immediate but minimal increase in risk, and O3 exhibits a time-delayed effect. The study integrates satellite data to provide a comprehensive view of pollution levels and utilizes community-level asthma patient data for informed policy development. Additionally, it identifies indoor pollution as a significant factor, given that urban families spend 80-90% of their time indoors, emphasizing its impact on asthma prevalence. The study also determines emission regions through reverse modeling.

The analysis of the Air Quality Index (AQI) is currently a popular subject in the area of sustainable research, as it is crucial for investigating and analysing the effects of air pollutants on human health in urban environments. It has been identified over the last decade that airborne pollution has become a critical issue and will remain an important concern in India in the coming years. In recent years, a variety of models and algorithms utilizing big data techniques have been developed for the analysis of air quality data. In this paper, we suggest monitoring and feature analysis of air quality data using a graph database. The research aims to analyse the annual and seasonal variations of AQI over a 10-year period between 2015–2024 from daily averaged concentration data of key air pollutants for 5 metro cities of India. The trends shown by all the cities have been compared to understand the seasonal variations in the average Air quality index. The variations of Average AQI in different severity classes in the cities also provide in-depth analysis of the trends. The findings from this analysis yield highly valuable information to assist in air pollution control, consequently leading to substantial societal and technical impacts. Finally, we offer a perspective on the future of air quality analysis, presenting some promising and challenging concepts. The results of this study can promote a more effectual environment monitoring system to detect drastic or unusual changes in atmosphere through the use of modern technologies.

Domain knowledge-enhanced multi-spatial multi-temporal [formula omitted] forecasting with integrated monitoring and reanalysis data

Short-term interventions may create a false sense of progress in addressing air quality issues. We find that despite the implementation of several short-term strategies during the coronavirus disease 2019 (COVID-19) pandemic, such as traffic restrictions, the anticipated improvements in air quality were not realized. On the contrary, the regional transportation of pollutants and the formation of secondary aerosols may still influence the air quality. Through a comprehensive analysis of air quality data collected before, during, and after the interventions in COVID-19, employing nonlinear statistical methods to assess changes in key pollutants, it can be inferred that the long-term memory mechanisms, nonlinear dynamic mechanisms of the atmospheric system play a crucial role in the evolution of air pollutants. Thus, the short-term human intervention did not significantly improve urban air quality. Besides, when pollution processes occur, the reason may not be unique, we must comprehensively interpret it. Policymakers are encouraged to consider more comprehensive and long-term strategies that integrate continuous monitoring and evaluation of air quality interventions.

Based on the analysis of air quality data in Yunnan Province from 2015 to 2020, combined with spatial interpolation analysis and geographic detector factor analysis, the spatial–temporal evolution characteristics of air quality in Yunnan Province have been studied, and the main driving factors, the mechanisms, and the impact of regional COVID-19 control measures affecting air quality have been discussed. The results show that the air quality in Yunnan Province was generally good (superior rate > 98%) from 2015 to 2020, that the Air Quality Index (AQI) value is better in the wet season than in the dry season, and that the concentration of major pollutants shows a decreasing trend. AQI values are spatially high in the east and low in the west. The relative humidity, precipitation, population density, building construction area, and civil vehicles have a greater degree of explanation for the spatial differentiation of AQI, whereas the synergistic influence (maximum value 0.92) of socio-economic factors and meteorological factors is significantly greater than that of a single factor (maximum value 0.80) by the geographic detector model. The control measures for COVID-19 in 2020 reduced the concentration of major pollutants in the atmosphere to a certain extent. Controlling regional air pollution in urban agglomerations in low-latitude plateau areas can improve their air quality by reducing human activities. However, the control of O3 concentration is more complex, and more restrictive factors need to be considered. The results will provide a scientific basis for the prevention and control of air pollution in plateau cities.

Multivariate analysis of particulate sulfate and other air quality variables by principal components-Part I: Annual data from Los Angeles and New York

A comparison of ground-level air quality data with New York State Department of Environmental Conservation monitoring stations data in South Bronx, New York

As sensing technologies become increasingly distributed and democratized, citizens and novice users are becoming responsible for the kinds of data collection and analysis that have traditionally been the purview of professional scientists and analysts. Leveraging this citizen engagement effectively, however, requires not only tools for sensing and data collection but also mechanisms for understanding and utilizing input from both novice and expert stakeholders. When successful, this process can result in actionable findings that leverage and engage community members and build on their experiences and observations. We explored this process of knowledge production through several dozen interviews with novice community members, scientists, and regulators as part of the design of a mobile air quality monitoring system. From these interviews, we derived design principles and a framework for describing data collection and knowledge generation in citizen science settings, culminating in the user-centered design of a system for community analysis of air quality data. Unlike prior systems, ours breaks analysis tasks into discrete mini-applications designed to facilitate and scaffold novice contributions. An evaluation we conducted with community members in an area with air quality concerns indicates that these mini-applications help participants identify relevant phenomena and generate local knowledge contributions.

The article presents an approach to pollution data for EU countries correlated to the Summary Innovation Index (SII). The analysis is based on data from 2015 to 2020. It compares pollution with health problems and deaths, and innovation. As it will be shown, it is a significant issue for EU countries. The approach is connected with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$CO_{2}$</tex> emission and its influence on health and people's attitudes toward pollution and climate change.

Differential health and economic impacts from the COVID-19 lockdown between the developed and developing countries: Perspective on air pollution

Nowadays, with the continuous development of big data technology, various industries use big data technology to process and mine massive data, and realize the value of data efficiently. In terms of air quality data processing, big data technology can also play a certain advantage. The platform is based on big data technology to design an air quality data analysis and prediction platform including data layer, business layer, interaction layer and visualization platform. Data is cleaned, calibrated, and stored in the data layer to ensure data consistency, integrity, and security. The air quality data is analyzed and predicted at the business layer. The interaction layer includes the functions of algorithm management, data query, and the data visualization platform provides intuitive information display. This design is a significant application for fully exploiting environmental data information. It has powerful data processing functions and scalability, which is a reliable data analysis and prediction platform.

The issue of urban air quality in China has become increasingly significant due to industrialization and rapid urbanization. Although PM2.5 is the major air pollutant in most cities of northern China and has a direct negative impact on human health, there is a problem of under-representativeness at Chinese monitoring stations. In some cities, due to the relatively fewer national control stations and the fact that the stations are located closer to pollution sources, under the current assessment system, the monitoring data are not sufficient for the fairness of air quality assessment in different cities. In this article, the multispectral data of Landsat 8 data, air quality data, and meteorological data from ground monitoring stations have been integrated together and imported to different PM2.5-estimation models established based on the multi-layer back propagation neural network (MLBPN), support vector regression (SVR), and random forest (RF), respectively. According to the evaluation indices of R2, RMSE, and ME, the estimation model based on the MLBPN revealed the best PM2.5 estimation results and was therefore employed for the regional representativeness analysis in the study area of Xi’an, Shaanxi, China. The annual average PM2.5 concentration in the study area is depicted after error correction using Kriging interpolation, which can be further used to evaluate and analyze the representativeness of monitoring stations in Xi’an. By calculating the difference between the actual station annual average and the annual average of estimated PM2.5 concentration in the whole region, it can be found that the regional annual average value of PM2.5 in Xi’an is overestimated. To sum up, this article proposes a feasible method for the spatial positioning of the air quality monitoring stations to be established.

In the last 5 yr, the capabilities of earth-observing satellites and the technological tools to share and use satellite data have advanced sufficiently to consider using satellite imagery in conjunction with ground-based data for urban-scale air quality monitoring. Satellite data can add synoptic and geospatial information to ground-based air quality data and modeling. An assessment of the integrated use of ground-based and satellite data for air quality monitoring, including several short case studies, was conducted. Findings identified current U.S. satellites with potential for air quality applications, with others available internationally and several more to be launched within the next 5 yr; several of these sensors are described in this paper as illustrations. However, use of these data for air quality applications has been hindered by historical lack of collaboration between air quality and satellite scientists, difficulty accessing and understanding new data, limited resources and agency priorities to develop new techniques, ill-defined needs, and poor understanding of the potential and limitations of the data. Specialization in organizations and funding sources has limited the resources for cross-disciplinary projects. To successfully use these new data sets requires increased collaboration between organizations, streamlined access to data, and resources for project implementation.

Brain Fog: Does Air Pollution Make Us Less Productive?

Over the last three decades, air pollution has become a major environmental challenge in many of the fast growing cities in China, including Beijing. Given that any long-term exposure to high-levels of air pollution has devastating health consequences, accurately monitoring and reporting air pollution information to the public is critical for ensuring public health and safety and facilitating rigorous air pollution and health-related scientific research. Recent statistical research examining China’s air quality data has posed questions regarding data accuracy, especially data reported during the Blue Sky Day (BSD) period (2000 – 2012), though the accuracy of publicly available air quality data in China has improved gradually over the recent years (2013 – 2017). To the best of our understanding, no attempt has been made to re-estimate the air quality data during the BSD period. In this paper, we put forward a machine-learning model to re-estimate the official air quality data during the BSD period of 2008 – 2012, based on the PM2.5 data of the Beijing US Embassy, and the proxy data covering Aerosol Optical Depth (AOD) and meteorology. Results have shown that the average re-estimated daily air quality values are respectively 64% and 61% higher than the official values, for air quality index (AQI) and AQI equivalent PM2.5, during the BSD period of 2008 to 2012. Moreover, the re-estimated BSD air quality data exhibit reduced statistical discontinuity and irregularity, based on our validation tests. The results suggest that the proposed data re-estimation methodology has the potential to provide more justifiable historical air quality data for evidence-based environmental decision-making in China.