Showing papers on "Air quality index published in 2022"
TL;DR: In this article , an IoT enabled Environmental Toxicology for Air Pollution Monitoring using Artificial Intelligence technique (ETAPM-AIT) is proposed to improve human health by using Artificial Algae Algorithm (AAA) based Elman Neural Network (ENN) model.
Abstract: In past decades, the industrial and technological developments have increased exponentially and accompanied by non-judicial and un-sustainable utilization of non-renewable resources. At the same time, the environmental branch of toxicology has gained significant attention in understanding the effect of toxic chemicals on human health. Environmental toxic agents cause several diseases, particularly high risk among children, pregnant women, geriatrics and clinical patients. Since air pollution affects human health and results in increased morbidity and mortality increased the toxicological studies focusing on industrial air pollution absorbed by the common people. Therefore, it is needed to design an automated Environmental Toxicology based Air Pollution Monitoring System. To resolve the limitations of traditional monitoring system and to reduce the overall cost, this paper designs an IoT enabled Environmental Toxicology for Air Pollution Monitoring using Artificial Intelligence technique (ETAPM-AIT) to improve human health. The proposed ETAPM-AIT model includes a set of IoT based sensor array to sense eight pollutants namely NH3, CO, NO2, CH4, CO2, PM2.5, temperature and humidity. The sensor array measures the pollutant level and transmits it to the cloud server via gateways for analytic process. The proposed model aims to report the status of air quality in real time by using cloud server and sends an alarm in the presence of hazardous pollutants level in the air. For the classification of air pollutants and determining air quality, Artificial Algae Algorithm (AAA) based Elman Neural Network (ENN) model is used as a classifier, which predicts the air quality in the forthcoming time stamps. The AAA is applied as a parameter tuning technique to optimally determine the parameter values of the ENN model. In-order to examine the air quality monitoring performance of the proposed ETAPM-AIT model, an extensive set of simulation analysis is performed and the results are inspected in 5, 15, 30 and 60 min of duration respectively. The experimental outcome highlights the optimal performance of the proposed ETAPM-AIT model over the recent techniques.
89 citations
TL;DR: Wang et al. as mentioned in this paper used an extended ensemble learning of the space-time extremely randomized trees (STET) model, together with ground-based observations, remote sensing products, atmospheric reanalysis, and an emission inventory, to estimate ground-level ozone from solar radiation intensity and surface temperature.
87 citations
TL;DR: Wang et al. as discussed by the authors investigated the contribution of green credit to improving air quality in China and employed the bootstrap sub-sample rolling-window Granger causality test to investigate the dynamic relationship between GC and air quality.
72 citations
TL;DR: Wang et al. as discussed by the authors investigated the change in air pollution (focusing on the air quality index (AQI), six ambient air pollutants nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), carbon monoxide (CO), particulate matter with aerodynamic diameters ≤10 μm (PM10) and ≤2.5 μm(PM2.
69 citations
TL;DR: Based on the population, economic, land, and social dimensions, Wang et al. as mentioned in this paper analyzed the relationship between China's urbanization and air pollution using fully modified least squares, Granger causality test, impulse response functions, and variance decomposition.
55 citations
TL;DR: In this article , the authors investigated the impacts of COVID-19 lockdown measures on annual carbon emissions globally, focusing on 47 greatly affected countries and their 105 cities by December 2020, and showed that while the lockdown measures significantly reduced carbon emissions, several countries and cities observed this reduction as temporary because strict lockdown measures were not imposed for extended periods in 2020.
51 citations
TL;DR: Li et al. as mentioned in this paper applied the difference-in-differences method to explore the local and spillover impact of the carbon emissions trading policy (2011) on China's carbon emissions and air quality, based on city-level data.
49 citations
TL;DR: Wang et al. as discussed by the authors developed a novel artificial intelligence approach by integrating spatio-temporally weighted information into the missing extra-trees and deep forest models to first fill the satellite data gaps and increase data availability by 49% and then derive daily 1 km surface NO2 concentrations over mainland China with full spatial coverage (100%) for the period 2019-2020 by combining surface No2 measurements, satellite tropospheric NO2 columns derived from TROPOMI and OMI, atmospheric reanalysis, and model simulations.
Abstract: Nitrogen dioxide (NO2) at the ground level poses a serious threat to environmental quality and public health. This study developed a novel, artificial intelligence approach by integrating spatiotemporally weighted information into the missing extra-trees and deep forest models to first fill the satellite data gaps and increase data availability by 49% and then derive daily 1 km surface NO2 concentrations over mainland China with full spatial coverage (100%) for the period 2019–2020 by combining surface NO2 measurements, satellite tropospheric NO2 columns derived from TROPOMI and OMI, atmospheric reanalysis, and model simulations. Our daily surface NO2 estimates have an average out-of-sample (out-of-city) cross-validation coefficient of determination of 0.93 (0.71) and root-mean-square error of 4.89 (9.95) μg/m3. The daily seamless high-resolution and high-quality dataset “ChinaHighNO2” allows us to examine spatial patterns at fine scales such as the urban–rural contrast. We observed systematic large differences between urban and rural areas (28% on average) in surface NO2, especially in provincial capitals. Strong holiday effects were found, with average declines of 22 and 14% during the Spring Festival and the National Day in China, respectively. Unlike North America and Europe, there is little difference between weekdays and weekends (within ±1 μg/m3). During the COVID-19 pandemic, surface NO2 concentrations decreased considerably and then gradually returned to normal levels around the 72nd day after the Lunar New Year in China, which is about 3 weeks longer than the tropospheric NO2 column, implying that the former can better represent the changes in NOx emissions.
42 citations
TL;DR: In this article , the authors analyzed data from ground-based sensors, satellites, and atmospheric models to better understand the March 2021 dust storm event, which significantly deteriorated air quality over a large area of East Asia.
41 citations
TL;DR: In this article, the authors analyzed data from ground-based sensors, satellites, and atmospheric models to better understand the March 2021 dust storm event, which significantly deteriorated air quality over a large area of East Asia.
41 citations
TL;DR: In this paper , a grey spatio-temporal model was proposed to quantitatively analyze the spillover and momentum effects of the COVID-19 lockdown policy on the concentration of PM2.5 (particulate matter of diameter less than 2.5 μm) in Wuhan during a pandemic lockdown from 23 January to 8 April 2020, and the post-pandemic period from 9 April 2020 to 17 October 2020 inclusive.
Abstract: This paper proposes a novel grey spatiotemporal model and quantitatively analyzes the spillover and momentum effects of the COVID-19 lockdown policy on the concentration of PM2.5 (particulate matter of diameter less than 2.5 μm) in Wuhan during the COVID-19 pandemic lockdown from 23 January to 8 April 2020 inclusive, and the post-pandemic period from 9 April 2020 to 17 October 2020 inclusive. The results suggest that the stringent lockdowns lead to a reduction in PM2.5 emissions arising from a momentum effect (9.57-18.67%) and a spillover effect (7.07-27.60%).
TL;DR: Wang et al. as mentioned in this paper reviewed the latest scientific literature on indoor ventilation modes and manuals of various countries, identified characteristics of different ventilation modes, and evaluated effects in different application occasions, wherefore to further propose their main limitations and solutions in the epidemic era.
TL;DR: In this article , the authors examined the history of air pollution under different stages of urbanization in typical high-, mid-, and low-income countries and summarized the general understanding of the relationship between urbanization and air pollution.
TL;DR: Zhang et al. as mentioned in this paper used a regulated model to explore the impact mechanism of air pollution perception on young talent urban settlement intentions, and they found that air pollution perceptions have a significant impact on urban dwelling willingness of young talent, and this impact is achieved through the intermediary effect of residential satisfaction.
Abstract: In recent years, with the public paying more and more attention to the problem of air pollution, the impact of air quality on migration has gradually become a growing concern. However, in the current context of cities’ efforts to “attract talent” in China, research on the impact of air pollution on the flow or dwelling willingness of young talent is relatively limited. Based on the theory of planned behavior and from the perspective of subjective perception, this paper uses a regulated model to explore the impact mechanism of air pollution perception on young talent urban settlement intentions. Taking Hangzhou as a case, this study surveyed 987 individuals who were classified as young talent to explore the impact of air pollution perception on urban settlement intentions in China. The research shows that air pollution perception has a significant impact on young talent urban settlement intentions, and this impact is achieved through the intermediary effect of residential satisfaction. Place attachment of young talent to cities cannot significantly regulate the impact of air pollution perception on residential satisfaction, but it can significantly regulate the relationship between residential satisfaction and urban settlement intentions. That is to say, although place attachment cannot reduce the decline in residential satisfaction brought by air pollution perception, it can weaken the negative impact of air pollution perception on dwelling willingness through a decline in residential satisfaction. This paper contributes to a deeper understanding of the relationship between air quality and young talent settlement intentions.
TL;DR: In this paper , an ANSYS FLUENT software was used to simulate the working environment of the dust removal fan under different air volumes, and it was shown that the optimal exhaust volume was Qs = 300 m3/min.
TL;DR: In this paper , the authors used a China-focused integrated assessment model and a dynamic emission projection model to project future Chinese air quality in scenarios spanning a range of global climate targets and national clean-air actions.
TL;DR: In this paper , the authors have published the first global update to its 2005 air quality guidelines based on a significantly improved body of evidence, which summarizes the purpose and rationale of the quantitative air quality guideline and interim target levels for six key pollutants: particulate matter 2.5, particle matter 10, sulfur dioxide, nitrogen dioxide, ozone, and carbon monoxide.
Abstract: Air pollution is a leading cause of death globally and has resulted in the loss of millions of healthy years of life. Moreover, the health burden has fallen disproportionately upon people in many low- and middle-income countries, where air quality continues to deteriorate. To assist authorities and civil society in improving air quality, World Health Organization has published the first global update to its 2005 air quality guidelines based on a significantly improved body of evidence. To facilitate the implementation of the World Health Organization Global Air Quality Guideline recommendations, this article summarizes the purpose and rationale of the quantitative air quality guidelines and interim target levels for six key pollutants: particulate matter 2.5, particulate matter 10, sulfur dioxide, nitrogen dioxide, ozone, and carbon monoxide. In addition, good practice statements are established for the management of pollutants of concern that lack sufficient evidence to substantiate numerical targets.
TL;DR: In this article , the authors investigated six years of air pollution data from 23 Indian cities for air quality analysis and prediction, and five machine learning models were employed to predict air quality.
Abstract: The survival of mankind cannot be imagined without air. Consistent developments in almost all realms of modern human society affected the health of the air adversely. Daily industrial, transport, and domestic activities are stirring hazardous pollutants in our environment. Monitoring and predicting air quality have become essentially important in this era, especially in developing countries like India. In contrast to the traditional methods, the prediction technologies based on machine learning techniques are proved to be the most efficient tools to study such modern hazards. The present work investigates six years of air pollution data from 23 Indian cities for air quality analysis and prediction. The dataset is well preprocessed and key features are selected through the correlation analysis. An exploratory data analysis is exercised to develop insights into various hidden patterns in the dataset and pollutants directly affecting the air quality index are identified. A significant fall in almost all pollutants is observed in the pandemic year, 2020. The data imbalance problem is solved with a resampling technique and five machine learning models are employed to predict air quality. The results of these models are compared with the standard metrics. The Gaussian Naive Bayes model achieves the highest accuracy while the Support Vector Machine model exhibits the lowest accuracy. The performances of these models are evaluated and compared through established performance parameters. The XGBoost model performed the best among the other models and gets the highest linearity between the predicted and actual data.
TL;DR: In this paper , the authors present a comprehensive overview of OA sources in Europe with a unique combination of high time resolution and long-term data coverage (9-36 months), providing essential information to improve/validate air quality, health impact, and climate models.
TL;DR: In this paper , the influence of reduction in emissions on the inherent temporal characteristics of PM2.5 and NO2 concentration time series in six urban cities of India is assessed by computing the Hurst exponent using Detrended Fluctuation Analysis (DFA) during the lockdown period (March 24-April 20, 2020) and the corresponding period during the previous two years (i.e., 2018 and 2019).
Abstract: The influence of reduction in emissions on the inherent temporal characteristics of PM2.5 and NO2 concentration time series in six urban cities of India is assessed by computing the Hurst exponent using Detrended Fluctuation Analysis (DFA) during the lockdown period (March 24–April 20, 2020) and the corresponding period during the previous two years (i.e., 2018 and 2019). The analysis suggests the anticipated impact of confinement on the PM2.5 and NO2 concentration in urban cities, causing low concentrations. It is observed that the original PM2.5 and NO2 concentration time series is persistent but filtering the time series by fitting the autoregressive process of order 1 on the actual time series and subtracting it changes the persistence property significantly. It indicates the presence of linear correlations in the PM2.5 and NO2 concentrations. Hurst exponent of the PM2.5 and NO2 concentration during the lockdown period and previous two years shows that the inherent temporal characteristics of the short-term air pollutant concentrations (APCs) time series do not change even after withholding the emissions. The meteorological variations also do not change over the three time periods. The finding helps in developing the prediction models for future policy decisions to improve urban air quality across cities.
TL;DR: In this article , a 0-D observation-based photochemical box model was used to assess the sources and sinks of Hox radicals and O3, and the OH reactivity (KOH) and ozone formation potential (OFP) of major VOC groups.
TL;DR: Wang et al. as mentioned in this paper evaluated the effect of China's stringent clean air actions on its energy use and CO 2 emissions from 2013-2020, finding that widespread phase-out and upgrades of outdated, polluting, and inefficient combustion facilities during clean-air actions have promoted the transformation of the country's energy system.
Abstract: Abstract Climate change mitigation measures can yield substantial air quality improvements while emerging clean air measures in developing countries can also lead to CO 2 emission mitigation co-benefits by affecting the local energy system. Here, we evaluate the effect of China’s stringent clean air actions on its energy use and CO 2 emissions from 2013-2020. We find that widespread phase-out and upgrades of outdated, polluting, and inefficient combustion facilities during clean air actions have promoted the transformation of the country’s energy system. The co-benefits of China’s clean air measures far outweigh the additional CO 2 emissions of end-of-pipe devices, realizing a net accumulative reduction of 2.43 Gt CO 2 from 2013-2020, exceeding the accumulated CO 2 emission increase in China (2.03 Gt CO 2 ) during the same period. Our study indicates that China’s efforts to tackle air pollution induce considerable climate benefit, and measures with remarkable CO 2 reduction co-benefits deserve further attention in future policy design.
TL;DR: In this paper , the establishment of automatic air quality monitoring stations as a quasi-natural experiment was used to evaluate the effect of monitoring stations on green innovation of listed companies in China.
TL;DR: In this paper , a 3D CNN-GRU model was applied to air pollution observations, especially PM2.5 level, collected from several AQ stations across the city of Tehran, Iran, from 2016 to 2019.
TL;DR: In this article , monetary impact values of exhaust and non-exhaust emissions emitted from internal combustion engine vehicles (ICEVs) and their equivalent EVs from an economic-environmental perspective, expressed as monetary impact value, were calculated according to the emission factors and damage costs of these pollutants.
TL;DR: A review of air quality research focusing mainly on developments over the past decade is provided in this article , which provides perspectives on current and future challenges as well as research needs for selected key topics.
Abstract: Abstract. This review provides a community's perspective on air quality research focusing mainly on developments over the past decade. The article provides perspectives on current and future challenges as well as research needs for selected key topics. While this paper is not an exhaustive review of all research areas in the field of air quality, we have selected key topics that we feel are important from air quality research and policy perspectives. After providing a short historical overview, this review focuses on improvements in characterizing sources and emissions of air pollution, new air quality observations and instrumentation, advances in air quality prediction and forecasting, understanding interactions of air quality with meteorology and climate, exposure and health assessment, and air quality management and policy. In conducting the review, specific objectives were (i) to address current developments that push the boundaries of air quality research forward, (ii) to highlight the emerging prominent gaps of knowledge in air quality research, and (iii) to make recommendations to guide the direction for future research within the wider community. This review also identifies areas of particular importance for air quality policy. The original concept of this review was borne at the International Conference on Air Quality 2020 (held online due to the COVID 19 restrictions during 18–26 May 2020), but the article incorporates a wider landscape of research literature within the field of air quality science. On air pollution emissions the review highlights, in particular, the need to reduce uncertainties in emissions from diffuse sources, particulate matter chemical components, shipping emissions, and the importance of considering both indoor and outdoor sources. There is a growing need to have integrated air pollution and related observations from both ground-based and remote sensing instruments, including in particular those on satellites. The research should also capitalize on the growing area of low-cost sensors, while ensuring a quality of the measurements which are regulated by guidelines. Connecting various physical scales in air quality modelling is still a continual issue, with cities being affected by air pollution gradients at local scales and by long-range transport. At the same time, one should allow for the impacts from climate change on a longer timescale. Earth system modelling offers considerable potential by providing a consistent framework for treating scales and processes, especially where there are significant feedbacks, such as those related to aerosols, chemistry, and meteorology. Assessment of exposure to air pollution should consider the impacts of both indoor and outdoor emissions, as well as application of more sophisticated, dynamic modelling approaches to predict concentrations of air pollutants in both environments. With particulate matter being one of the most important pollutants for health, research is indicating the urgent need to understand, in particular, the role of particle number and chemical components in terms of health impact, which in turn requires improved emission inventories and models for predicting high-resolution distributions of these metrics over cities. The review also examines how air pollution management needs to adapt to the above-mentioned new challenges and briefly considers the implications from the COVID-19 pandemic for air quality. Finally, we provide recommendations for air quality research and support for policy.
TL;DR: In this article , the authors employed spatial simultaneous equations models with the Generalized Spatial Three-Stage Least Squares (GS3SLS) method to evaluate the interrelationship between economic growth and air pollution under local government competition for China's 2 + 26 cities from 2007 to 2016.
TL;DR: In this article , the authors implemented a UAV network based on IoT (Internet of Things) and a cloud server for the smart city for tracking the air quality of the landfill sites in real-time and alerting the UAV for capturing the visuals from the camera for detecting the exact cause of the pollutant.
TL;DR: In this paper , the authors present a state-of-the-art anthropogenic emission inventory developed for the European domain for an 18-year time series (2000-2017) at a 0.05∘ × 0.1∘ grid resolution, specifically designed to support air quality modelling.
Abstract: Abstract. This paper presents a state-of-the-art anthropogenic emission inventory developed for the European domain for an 18-year time series (2000–2017) at a 0.05∘ × 0.1∘ grid resolution, specifically designed to support air quality modelling. The main air pollutants are included: NOx, SO2, non-methane volatile organic compounds (NMVOCs), NH3, CO, PM10 and PM2.5, and also CH4. To stay as close as possible to the emissions as officially reported and used in policy assessment, the inventory uses the officially reported emission data by European countries to the UN Framework Convention on Climate Change, the Convention on Long-Range Transboundary Air Pollution and the EU National Emission Ceilings Directive as the basis where possible. Where deemed necessary because of errors, incompleteness or inconsistencies, these are replaced with or complemented by other emission data, most notably the estimates included in the Greenhouse gas Air pollution Interaction and Synergies (GAINS) model. Emissions are collected at the high sectoral level, distinguishing around 250 different sector–fuel combinations, whereafter a consistent spatial distribution is applied for Europe. A specific proxy is selected for each of the sector–fuel combinations, pollutants and years. Point source emissions are largely based on reported facility-level emissions, complemented by other sources of point source data for power plants. For specific sources, the resulting emission data were replaced with other datasets. Emissions from shipping (both inland and at sea) are based on the results from a separate shipping emission model where emissions are based on actual ship movement data, and agricultural waste burning emissions are based on satellite observations. The resulting spatially distributed emissions are evaluated against earlier versions of the dataset as well as against alternative emission estimates, which reveals specific discrepancies in some cases. Along with the resulting annual emission maps, profiles for splitting particulate matter (PM) and NMVOCs into individual components are provided, as well as information on the height profile by sector and temporal disaggregation down to the hourly level to support modelling activities. Annual grid maps are available in csv and NetCDF format (https://doi.org/10.24380/0vzb-a387, Kuenen et al., 2021).
TL;DR: In this paper, 30 PurpleAir II sensors (12 outdoor and 18 indoor) were deployed in partnership with community members living adjacent to a major interstate freeway from December 2017-June 2019.