Air Quality In Cities Research Articles

Air quality assessment model

This study examines the application of machine learning methods to predict air quality in Brisbane, Australia. The main attention is paid to the creation of a model capable of predicting the concentration of PM10 suspended particles based on meteorological data. In the course of the work, a statistical analysis of the factors influencing the level of pollution was carried out, and a random forest model was developed and tested. The results showed that the model is able to explain about 69% of the variation in PM10 concentration, and also identified key meteorological parameters such as air temperature and wind speed that have the greatest impact on the concentration of pollutants. The data obtained can be used to improve the monitoring and management of air quality in cities, which in the future may contribute to reducing the harmful effects of pollution on public health.

Тревожное экологическое будущее и экономика России

The key issues of the formation and development of the environmental system in the USSR - Russian Federation (1988–2022) are briefly reviewed, and it is substantiated that official data are insufficient for analyzing the state of the environment in Russia. Based on data on air quality in cities and pollution of water bodies, the environmental situation in Russia is classified as unfavorable, and the absence of significant positive changes is shown. Disadvantages in the development of federal environmental projects and other causes of environmental problems are considered. Suggestions for exiting the environmental depression are given.

Air pollution and economic growth in Dubai a fast-growing Middle Eastern city

This paper discusses the impact of rapid economic development on air quality in the Emirate of Dubai, United Arab Emirates (UAE). Dubai is one of the fastest-growing cities in the world, with a population increase of approximately 80× over the last 60 years. The concentrations of five criteria air pollutants (CAPs) including carbon monoxide (CO), nitrogen dioxide (NO2), particulate matter with diameter less than 10 μm (PM10), ozone (O3) and sulphur dioxide (SO2) were studied from 2013 to 2021 at 14 regulatory monitoring stations. Results show that the biggest improvements in air pollution are for the primary air pollutants NO2 and SO2, with reductions of 54% and 93% respectively over the period studied. Gross domestic product (GDP), population growth and energy consumption are significantly and negatively correlated with NO2 and SO2 and strongly and positively correlated with PM10. CO is positively correlated with the number of buildings completed, while the results for O3 are inconclusive. Trends in NO2 and SO2 indicate that these two pollutants are decoupled from economic development, supporting, with caution, the Environmental Kuznets Curve hypothesis on the relationship between economic growth and environmental degradation. The improvement in the city's air quality is due to the effective implementation of local environmental policies, unaffected by large-scale development and urbanization. The monthly assessments of Dubai's air pollution for 2019 and 2020 show a 3–16% COVID-related improvement in the levels of the studied air pollutants, except for ozone, which increased by an average of 8%.

Study on the Positive Effects of Environmental Rule of Law Advancement on Environmental Governance in the Age of Artificial Intelligence

Abstract In the era of artificial intelligence, the importance of environmental rule of law for ecological governance is becoming increasingly important. This study explores how artificial intelligence can contribute to environmental management, especially the prediction and analysis of ambient air quality through the random forest algorithm. The study aims to assess and predict the changes of ambient air quality in Chinese cities and provide a scientific basis for environmental governance. Methodologically, a random forest model was used to analyze the relationship between ambient air quality and multiple factors. The results showed that the random forest model was effective in predicting air quality, in which the level of urban economic development showed a significant correlation with air quality, and the PM2.5 concentration in cities with a high level of economic development was significantly higher than that in the towns with a low to medium level of economic growth, as the PM2.5 concentration decreased from 43.854 μg/m3 to 33.941 μg/m3. In addition, seasonal variations had a significant effect on air quality. It is concluded that the random forest model is an effective tool to provide accurate data support for environmental rule of law and governance, which helps to formulate more accurate ecological protection policies.

Analysis of Factors Influencing Air Quality in Major Cities in China

Analysis of Factors Influencing Air Quality in Major Cities in China

Citizens’ satisfaction on the urban environmental quality

The research goal was to estimate public satisfaction with ambient air quality in an industrial city in Russia and describe certain peculiarities in the perception of related health risks. The resulting estimates were used as grounds for describing urban environmental quality. The empirical base of the research was provided by the results of a social study performed in a Russian city with over a million inhabitants. That city was included into the “Clean air” Federal project. The data were collected by conducting an internet survey (n=544). Having analyzed these data, we detected that public satisfaction with ambient air quality was rather low (not more than 11% of the participants chose the variants “quite satisfied” or “fully satisfied”). Most respondents estimated ambient air quality as being “poor” or “very poor” (giving a 3-score estimate or even lower according to a 7-score scale). They also believed that air pollution produced negative effects on their health. People living in an industrial city tend to have alarmist perception of health risks. These risks seem uncontrollable and involuntary to them. Women are a more risk-sensitive group in comparison with men. These obtained data corroborate a hypothesis about a direct relationship between actual and perceived environmental pollution.

Identification of places with deteriorated air quality in city of Žilina in relation to road transport

The aim of the research is to identify places with deteriorated air quality in the selected city. After an analytical analysis of the data, it is possible to propose steps that can be used to actively contribute to the reduction of air pollution, especially due to road traffic. Places where deteriorated air quality was identified were identified on the basis of practical measurements. Given that the measurement route was designed in a built-up area of the city with a large number of pedestrians, the research was primarily focused on the identification of particulate matters (PM) and the concentration of carbon monoxide CO. The measurements were performed repeatedly on a pre-defined route. The measurements were carried out repeatedly during the morning rush hour when traffic was congested on the roads. Based on the processing and evaluation of the measurements, the sections where increased values of individual emissions were recorded were determined. In this way, it is possible to precisely identify places where air quality deteriorates. The research conclusions provide support for planning the optimization of air quality management policies towards the creation of sustainable cities. The research results present the possibilities of identifying problematic sections from the point of view of emissions production. Critical places with regard to the production of emissions can be connected to places where a permanently increased movement of vehicles is observed.

Connecting the Dots between Urban Morphology and the Air Quality of Cities under a Changing Climate: A Bibliometric Analysis

The expected increase in urbanization changes the morphology of urban areas. These changes affect the urban environment and impact several aspects, such as climate, energy, air quality, and socioeconomic factors, among others. Therefore, it is important to lead cities towards sustainable development. The goal of this paper is to understand which domains should be considered to assess the sustainability of cities, at an environmental level and with a focus on air quality, and how those domains are connected, based on a comprehensive literature review, which resulted in 1479 articles. The results show that “Urban Climate”, “Air Quality”, “Urban Morphology”, “Health”, “Energy” and “Mobility” are the most common domains in research, and that 39% of articles only assess one domain. It is possible to understand that though 96% of articles contain up to three domains, the great majority do not assess those domains in an integrated way. There are many studies that evaluate important domains of urban areas; however, they mostly evaluate these effects in isolation, and rarely in tandem. Moving forward, it is important to understand how to best connect the most relevant domains, under an integrated multicriteria approach, thus allowing a more complete assessment of the sustainability of urban areas.

Airborne desert dust in the Northern Adriatic area (Croatia): Different sources

During the implementation of the INTERREG IT-HR project ECOMOBILITY, whose one of the goals was to estimate the impact of ship emissions on air quality in the port city of Rijeka (Croatia) and Venice (Italy), two particular weekly samples were collected in Rijeka, during the first and the thirteen weeks of sampling, i.e. S01 (16.10.-23.10.2018) and S13 (24.04.-30.04.2019.), respectively. Both samples have similarities regarding species characteristic for desert dust contribution, but HYSPLIT analyses excluded Saharan desert to be the source of the S01 sample. Unlike Saharan dust, this sample had a high contribution of fine and ultrafine particles (>50 % and 9.8 %, respectively), as well as secondary inorganic (sulfates, ammonium) and organic (water soluble organic compounds - WSOC) aerosols. Detailed synoptic situation and HYSPLIT backward trajectories pointed out the Syrian Desert as the source of this collected sample. The same source was proved by MERRA-2 reanalysis of the desert dust emission. Although the Saharan dust episodes, mostly in precipitation, are well known in the Northern Adriatic area, this is the first time to indicate Syrian Desert as a source of airborne particulates. This assumption was confirmed with chemical species characteristic for the Syrian Desert, i.e. higher content of potassium from K- feldspar and phosphates.

Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting

The problem of air pollution threatens public health. Air quality forecasting can provide the air quality index hours or even days later, which can help the public to prevent air pollution in advance. Previous works focus on citywide air quality forecasting and cannot solve nationwide city forecasting problems, whose difficulties lie in capturing the latent dependencies between geographically distant but highly correlated cities. In this article, we propose the group-aware graph neural network (GAGNN), a hierarchical model for nationwide city air quality forecasting. The model constructs a city graph and a city group graph to model the spatial and latent dependencies between cities, respectively. GAGNN introduces a differentiable grouping network to discover the latent dependencies among cities and generate city groups. Based on the generated city groups, a group correlation encoding module is introduced to learn the correlations between them, which can effectively capture the dependencies between city groups. After the graph construction, GAGNN implements message passing mechanism to model the dependencies between cities and city groups. The evaluation experiments on two real-world nationwide city air quality datasets, including the China dataset and the US dataset, indicate that our GAGNN outperforms existing forecasting models.

Green Buildings in Pursuit of Healthy and Safe Human Living Environment

Abstract Emission of harmful substances into the atmosphere resulting from the combustion of fuels in the energy production process and road traffic intensity are a key determinants of poor air quality in cities and the creation of an unfriendly environment for people to live in, which has a significant impact on their safety and health. The first step to reducing emissions is to reduce energy consumption. The ecological effect resulting from the thermal modernization of existing residential building stock was estimated. Nature-based solutions were proposed to compensate for the lost green areas in favor of gray infrastructure in the form of green roofs and walls. The possibility of improving environmental conditions by introducing this type of solutions into the urban tissue was assessed. Depending on the type of vegetation, one m2 of green cover is able to absorb an average of 2.3 kg of CO2 and 0.2 kg of particulate matter from the air per year. Renewable energy sources are an important element of green buildings. Heat pump may be the most advantageous solution in minimizing emissions combined with low operating costs. Obtaining energy from geothermal sources would be equally beneficial in terms of reducing emissions, but there are risks changes in groundwater levels or soil damage. Solar energy is one of the leading renewable energy sources, especially in hot water installations, where it is possible to reduce energy consumption by up to 50%.

Analysis Of NOx, SO2, PM10, & CO Emission Loads From Motorized Vehicle at Collector’s Road Makassar City Using Tier 2 Method

The biggest factor in the global reduction in air quality in large cities is the transportation industry. This is due to the molecules that gasoline, the fuel used in the transportation sector, emits, including carbon monoxide (CO), nitrogen oxide (NOx), sulfur dioxide (SO2), and CO2 (carbon dioxide), as well as hydrocarbons (HC), which are sources of carbon dioxide. of emissions of air pollutants An imbalance results from the rising motorized vehicle sales from year to year combined with poor traffic conditions. In urban locations, several collector roads—the hub of community activity—often encounter congestion, especially during rush hours. Because of the pollution these motorized vehicles produce, the congestion they induce has a negative effect on the environment. This study aims to determine the daily and yearly emission loads for the parameters of NOx, SO2, PM10, and CO produced by motor vehicles with three types of vehicles, namely motorcycles (MC), Light Vehicles (LV), which are divided into the Sedan category, Minibus, Taxi, Public Transportation, then Heavy Vehicles (HV), which are divided into Pick-up, Jeep, micro bus, big bus, 2 axles truck, and 3 axles trucks categories, on Collector Street in Makassar City utilizing the Tier 2 method and show the form for mapping the distribution of NOx, SO2, PM10, and CO emissions from motor vehicles on Collector Street in Makassar City using the Geographic Information System application. The findings indicated that Jalan Hertasning produced the highest emission load from the NOx, SO2, PM10, and CO parameters. This is because of the significant role that Hertasning Road plays in Makassar City’s urban road network, which results in a high volume of daily vehicle traffic on this road section. The CO parameter, which accounts for 104041.57 tons of the total emission load from motor vehicles on Jalan Hertasning, per year.

Towards benchmarking of urban air quality based on homogenous surface emission

Here, it is presented a possible methodology and experimental model for benchmarking of air quality in cities. The concept behind the methodology is that a city's inherent structure affects the potential for contaminant removal due to the resistance it poses to inflow. The approach is based on homogenous emission across the street surface network, representing a worst-case situation. Different levels of complexity can be used for benchmarking, making it valuable for evaluating different layouts. Additionally, an urban ventilation index suitable for these kinds of experimental studies has been suggested.

Assessing the Impact of COVID-19 Regulations on Air Quality in Asian Cities: A Time Series Analysis

Assessing the Impact of COVID-19 Regulations on Air Quality in Asian Cities: A Time Series Analysis

The ambient air quality standards, green innovation, and urban air quality: evidence from China

As China’s economy transitions, environmental issues have become a major concern. This study examines the impact of Ambient Air Quality Standards (AAQS) on urban air quality using panel data from 284 cities in China from 2006 to 2019. The study utilizes DID (Difference-in-Difference) models to analyze the regulatory effects of AAQS and its spatial spillover. Additionally, the serial multiple mediation models are constructed to investigate the role of green innovation. The findings reveal that the AAQS positively affects urban air quality, albeit with a notable “hysteresis effect.” Local implementation of AAQS worsens air quality in neighboring cities within a distance of 400 km, but beyond 400 km, the effect is reversed. Heterogeneity analysis shows that AAQS improves air quality in central cities, large-sized and medium-sized cities, cities with weak environmental governance, and resource-based cities. Mechanism tests suggest that AAQS may enhance urban air quality by promoting green innovation and optimizing industrial structure. Especially, either the energy-use effect or industrial-structure effect triggered by green innovation can contribute to the improvement of urban air quality.

A comparison of vehicle emissions control strategies for smart cities

Many studies have shown that air quality in cities is affected due to emissions of carbon from vehicles. As a result, policymakers (e.g., municipalities) intensely search for new ways to reduce air pollution due to its relation to health diseases. With this concern, connected vehicle technologies can leverage alternative on-road emissions control policies. The present investigation studies the impact on air pollution by (i) updating vehicles’ routes to avoid pollution exposure (route choice policy), (ii) updating vehicles’ speed limits (speed control policy), and (iii) considering electric vehicles (EVs). Vehicles are informed in advance about route conditions (i.e., on-road emissions) using the vehicular network. We found that by updating vehicle routes, 7.43% less CO emissions are produced within the evaluated region. Also, we find no evidence of significant emissions reductions in the case of limiting vehicles’ speed. Lastly, with 30% of EV penetration, safe CO emissions levels are reached.

Estimation and bootstrapping under spatiotemporal models with unobserved heterogeneity

Proposed herein is a novel spatiotemporal model to characterize the unobserved heterogeneity across individuals using quantile-function-based correlated random effects and heteroscedastic innovations in a general framework. This model can be used to explore the influence of space-specific factors on latent effects at different quantile levels by controlling for spatiotemporal effects. A two-stage estimation procedure is introduced in which (i) the method of moments is used to estimate spatiotemporal effects then (ii) quantile regression is used for individual effects. A hybrid double bootstrapping procedure is then proposed to approximate the asymptotic distributions of coefficient estimators. The validity of the estimation and bootstrapping is established theoretically and then confirmed by simulation studies, and the usefulness of the proposed model is demonstrated with a real example involving city air quality.

Predicting air quality in smart city using novel transfer learning based framework

Air quality is a matter of concern these days due to its adverse effect on human health. Multiple new air pollution monitoring and prediction stations are being developed in smart cities to tackle the issue. Recent advanced deep learning techniques show excellent performance for air quality predictions but need sufficient training data for model performance. The data insufficiency issue at a new station can be resolved using the proposed novel transfer learning-based framework to predict pollution concentration at the new station. The prediction ability at a new station can be significantly enhanced by this effective technology. The performance of the model is assessed on various stations in Delhi, India.

PM chemical characterization at a semi-arid urban environment in Central Mexico

The central part of Mexico, a semi-arid region, hosts many anthropogenic activities that might affect the air quality of the several large cities within; however, the processes that occur in its atmosphere are poorly understood. Here, we investigated the concentration and chemical composition of the particulate matter in the Metropolitan Area of Queretaro for one year, together with meteorological variables and other pollutants data. Seasonal concentrations were determined mainly by meteorology. The secondary components comprised the largest fraction of PM2.5, followed by resuspension and the typical primary combustion sources in an urban area. Regional processing played a very important role in the formation of secondary aerosols. Local oxidation producing nitrate and less oxidized organic aerosols occurred more rapidly at night, although photochemical production in the morning was also observed. These results represent the first report of long-term real-time chemical characterization with high time resolution in the region. In addition, they might be important for better understanding the production of secondary aerosols in other semi-arid urban areas.

Mobile Laboratory Investigations of Industrial Point Source Emissions during the MOOSE Field Campaign

Industrial emissions of trace gases and VOCs can be an important contributor to air quality in cities. Disentangling different point sources from each other and characterizing their emissions can be particularly challenging in dense industrial areas, such as Detroit, Dearborn and surrounding areas in Southeast Michigan (SEMI). Here, we leverage mobile measurements of trace gases and speciated volatile organic compounds (VOCs) to identify emitting sites. We characterize their complicated emissions fingerprints based on a core set of chemical ratios. We report chemical ratios for 7 source types including automakers, steel manufacturers, chemical refineries, industrial chemical use (cleaning; coatings; etc.), chemical waste sites, compressor stations, and more. The source dataset includes visits to over 85 distinct point sources. As expected, we find similarities between the different types of facilities, but observe variability between them and even at individual facilities day-to-day. Certain larger sites are better thought of as a collection of individual point sources. These results demonstrate the power of mobile laboratories over stationary sampling in dense industrial areas.