Air Quality Situation Research Articles

Impact of Air Quality Index of Pune City: A Review

This work aims to perform a comprehensive analysis of earlier studies on Pune City's Air Quality Index (AQI). The main objectives are to define and analyze the current state of knowledge on Pune's air quality, point out significant study gaps, and provide details regarding upcoming studies and policies. According to the literature review, the quality of air in Pune City is declining due to its growing population. The primary air adulterants responsible for high AQI readings are particulate matter (‘PM-10 and PM-2.5’), nitrogen dioxide (‘NO2’), sulfur dioxide (‘SO2’), carbon monoxide (‘CO’), and ozone (‘O3’). Research reveals that industrialization, urbanization, and auto emigration all have an impact on air quality. Meteorological and seasonal conditions greatly impact the AQI readings in Pune. Research is necessary to determine the long-term health impacts of increased AQI levels, with more attention to sensitive groups like children, the elderly, and those with pre-existing medical issues. Furthermore, studies examining the efficacy of present regulatory measures and putting forth creative ways to improve air quality are essential. Pune City's air quality situation is thoroughly described in this literature review, which adds to the body of knowledge already in existence. A comprehensive approach and multidisciplinary cooperation are needed to tackle the intricate problems related to air quality in metropolitan settings such as Pune, as the synthesis of existing information makes clear. In the end, this assessment seeks to encourage additional investigation and action to lessen the negative impacts of air pollution on Pune City's environment and general public health.

Weather Pollution Prediction Based on XGBoost and ARIMA: Evidence from Shanghai

As a matter of fact, the weather pollution prediction is a pretty important issue in environment analysis. With the rapid development of machine learning techniques and bigdata analysis, it is available to realize accurate prediction based on the state-of-art models. In recent years, the weather condition is pretty worse in Shanghai. With this in mind, the general situation of air quality in Shanghai was statistically analyzed using air pollution detection data such as API in Shanghai from 2014 to 2023, and the relationship between air quality influencing factors and the air quality index API was investigated based on the construction of the machine learning model and scenarios. With the analysis and the usage of XGBoost and ARIMA, one can get the future trends and prediction. According to the analysis, the impact factors are discussed as well as the current limitations are demonstrated. At the same time, the future prospects are discussed.

Investigation of climate change effects on Iraq dust activity using LSTM

Awareness about the impact of climate change on the change rate of dust phenomenon has become a crucial problem due to public health, climate, and air quality. It has been recognized that the Tigris- Euphrates alluvial plain is the main source of dust storms in the Middle East and southwest Asia. In this work, the effects of different variables, including minimum temperature (Tmin), maximum temperature (Tmax), precipitation (Pr), mean wind speed (MWS), humidity (H), and vegetation (Veg) on dust activity are investigated, and Aerosol optical depth (AOD) was predicted by Long Short-Term Memory (LSTM) from 2021 to 2040. HadGEM3-GC was selected to generate the climatic patterns under Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) scenarios until 2040. The Long Ashton Research Station Weather Generator (LARS-WG 6) software was used to downscale the General Circulation model (GCM). Moreover, Mann-Kendall and Sen's slope tests are utilized to assess a significant trend. Tmax, Tmin, Pr, and Veg were determined to predict AOD that had a highly close relationship with AOD (r = 0.865, R2 = 0.752, RMSE = 0.079, and MAE = 0.065). The results indicated that AOD would rise gradually in two upcoming decades with 10.5% and 15.2% related to base period (2000–2020) under two scenarios. Understanding of dust phenomenon of future prospective could help policymakers to provide a better situation of air quality.

Measures and Policies for Reducing PM Exceedances through the Use of Air Quality Modeling: The Case of Thessaloniki, Greece

The main aim of this study is the identification of the most appropriate measures and policies to combat particulate air pollution in the city of Thessaloniki, Greece through a methodology including the calculation of emissions by sector and the application of an air quality modeling system. The identification of the current air quality situation showed that the exceedances of the mean PM10 daily levels occur during the cold winter months with residential heating being the major contributor representing a 73% share of total PM10 emissions. Moreover, the effect of the biomass consumption for heating purposes on PM10 concentrations has been verified and quantified by implementing a regression model identifying that a PM10 emissions reduction by 45% would result in the elimination of the exceedances of the mean PM10 daily values. Based on the above, a set of cost-effective measures and policies were defined and their impact on PM10 emissions was estimated. Finally, the air quality modeling system was applied for the mitigation scenarios selected, leading to a significant reduction of 67% in the number of exceedances observed and ensuring compliance with the limit of 35 exceedances of the daily value limit per year pursuant to European Legislation.

The Historical Nexus between Air Quality and Economic Development: Evidence from China

This paper examines the relationship between economic development and air quality in China. After introducing the economic development process and air quality situation in China, a comprehensive literature review is carried out, in which several works that document research on the link between economic growth and pollution are examined critically. The findings of this study lend credence to the Environmental Kuznets Curve, which shows a correlation between rising levels of air pollution and expanding economies. After conducting research and analysis of China's economic growth after reforms, it was discovered that economic growth was a major contributor to environmental pollution. In addition, it contributed to the rise of the middle class, which increased the use of polluting fossil fuels. The study suggests that non-renewable energies, electric cars, and industrial technologies be implemented to reduce air pollution. Additionally, the study indicates that research be conducted regarding how responsible development can be achieved without necessarily having a negative impact on ecological environment.

Design and Fabrication of Small-Scale Urban Air Purification Tower

Air pollution has become a significant environmental issue in the twenty-first century, causing a negative impact on the quality of life and human health. Various international organizations have been working to address air pollution issues and provide potential solutions. One of the primary tools used to measure air quality is the Air Quality Index (AQI), which ranges from 0 to 500, indicating levels of different air pollutants in the ambient air. Particulate matter (PM) is one of the most hazardous air pollutants. Outdoor air purification towers are a promising technology to address air quality issues, and this project aims to design and fabricate an air purification tower for Karachi, biggest city of Pakistan and an industrial hub with significant air pollution issues. The lack of air purification devices throughout the country is a significant motivation behind this project, and the main objectives include observing the current situation of ambient air quality in Karachi, testing the performance of the air purification tower, and evaluating the feasibility of the project to be replicated on a larger scale. The project’s methodology includes a review of existing literature and engineering knowledge, followed by design calculations based on heat losses, velocities, and volume flow rates. A parametric study and economic analysis were used to select feasible dimensions, and a CAD model was developed. The report outlines the project’s chronology, including an introduction, literature review, assessment of air quality in Karachi, heat loss coefficient, velocity and flow rate calculations, and a parametric study to reach optimal dimensions for the model used in fabrication. The project’s significance lies in addressing air pollution issues and providing a solution for Karachi’s air quality problems. The success of this project could inspire further replication of air purification towers on a larger scale in Pakistan and other countries facing similar air pollution issues.

Modelling the Effects of Euro 7 Emission standard on Passenger Car Emissions in Germany

Road traffic represents a significant source of air pollution and greenhouse gases, particularly in urban areas. A new emission standard for passenger cars – Euro 7, coming into force in 2025 - is currently elaborated at European level to improve the air quality situation and thus the prerequisite for human well-being in the EU. The legislative proposal is expected to be published in 2022. This paper focusses on the evaluation of the effectiveness of Euro 7 standard with regard to vehicle fleet emissions in Germany. To this end, emission trend scenarios were generated for relevant exhaust gas components by applying the TREMOD model. Hereby, a strong reduction potential becomes apparent for hydrocarbon and nitrogen oxide emissions up to 2035. Assumptions laid down in this study need to be verified with real-world emission data when the new emission standard comes into force.

DATA DRIVEN FRAMEWORK FOR ANALYSIS OF AIR QUALITY LANDSCAPE FOR THE CITY OF LAHORE

Abstract. Several Pakistani cities are among the world’s most polluted. In the previous three years, air pollution in Lahore has been considerably over World Health Organization guideline levels, endangering the lives of the city’s more than 11 million citizens. In this paper, we investigate the city’s capability to combat air pollution by analyzing three essential aspects: (1) Data, (2) Capacity, and (3) Public awareness. Several studies have reported the need for expansion of the current air quality monitoring network. In this work, we also provide a context-aware location recommendation algorithm for installing the new air quality stations in Lahore. Data from four publicly available reference-grade continuous air quality monitoring stations and nine low-cost air quality measuring equipment are also analyzed. Our findings show that in order to measure and mitigate the effects of air pollution in Lahore, there is an urgent need for capacity improvement (installation of reference-grade and low-cost air quality sensors) and public availability of reliable air quality data. We further assessed public awareness by conducting a survey. The questionnaire results showed huge gaps in public awareness about the harms of the air quality conditions. Lastly, we provided a few recommendations for designing data-driven policies for dealing with the current apocalyptic air quality situation in Lahore.

Air Quality in Two Northern Greek Cities Revealed by Their Tropospheric NO2 Levels

In this article, we aim to show the capabilities, benefits, as well as restrictions, of three different air quality-related information sources, namely the Sentinel-5Precursor TROPOspheric Monitoring Instrument (TROPOMI) space-born observations, the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) ground-based measurements and the LOng Term Ozone Simulation-EURopean Operational Smog (LOTOS-EUROS) chemical transport modelling system simulations. The tropospheric NO2 concentrations between 2018 and 2021 are discussed as air quality indicators for the Greek cities of Thessaloniki and Ioannina. Each dataset was analysed in an autonomous manner and, without disregarding their differences, the common air quality picture that they provide is revealed. All three systems report a clear seasonal pattern, with high NO2 levels during wintertime and lower NO2 levels during summertime, reflecting the importance of photochemistry in the abatement of this air pollutant. The spatial patterns of the NO2 load, obtained by both space-born observations and model simulations, show the undeniable variability of the NO2 load within the urban agglomerations. Furthermore, a clear diurnal variability is clearly identified by the ground-based measurements, as well as a Sunday minimum NO2 load effect, alongside the rest of the sources of air quality information. Within their individual strengths and limitations, the space-borne observations, the ground-based measurements, and the chemical transport modelling simulations demonstrate unequivocally their ability to report on the air quality situation in urban locations.

Study on Air Quality and Its Annual Fluctuation in China Based on Cluster Analysis.

Exploring the spatial and temporal distribution characteristics of air quality has become an important topic for the harmonious development of human and nature. Based on the hourly data of CO, O3, NO2, SO2, PM2.5 and PM10 of 1427 air quality monitoring stations in China in 2016, this paper calculated the annual mean and annual standard deviation of six air quality indicators at each station to obtain 12 variables. Self-Organizing Maps (SOM) and K-means clustering algorithms were carried out based on MATLAB and SPSS Statistics, respectively. Kriging interpolation was used to get the clustering distribution of air quality and fluctuation in China, and Principal Component Analysis (PCA) was used to analyze the main factors affecting the clustering results. The results show that: (1) Most areas in China are low-value regions, while the high-value region is the smallest and more concentrated. Air quality in northern China is worse, and the annual fluctuations of the indicators are more dramatic. (2) Compared with AQI, AQFI has a strong indication significance for the comprehensive situation of air quality and its fluctuation. (3) The spatial distribution of SOM clustering results is more discriminative, while K-means clustering results have a large proportion of low-mean regions. (4) PM2.5, PM10 and CO are the main pollutants affecting air quality and fluctuation, followed by SO2, NO2 and O3.

Air Quality in Lombardy, Italy: An Overview of the Environmental Monitoring System of ARPA Lombardia

In this paper, we describe the structure and the features of the air quality and meteorological monitoring system adopted in the Lombardy region in Northern Italy. We are interested in describing which data the Regional Agency for Environmental Protection (ARPA Lombardia) must collect, how this process takes place and how they are disseminated to the public for institutional communication and research purposes. ARPA monitors the atmospheric conditions through a dense ground monitoring network composed mainly by permanent stations, but also by mobile samplers. We describe in a detailed fashion the structure of the network: how many stations the network consists of, their locations, which pollutants and weather events are monitored and with what frequency. Our main objective is to present to an international audience the case study of ARPA Lombardia and the main available public data, explicitly stating the sources of information and how to find them, and encouraging international researchers to deal with the subject. In spite of the significant and extensive efforts made to counteract the phenomenon of air pollution, the air quality recorded in the region is very poor, and the local authorities are struggling to comply with international regulations on the concentration of pollutants in the air, making Lombardy a relevant international case. In addition, we present in a synthetic and descriptive way, without any modeling ambition, some data observed in the last years in Lombardy regarding meteorology and the main pollutants (oxides and particulate matters). The empirical descriptive results have been obtained by analyzing sample data provided by ARPA Lombardia through the same sources described in the sections dedicated to the Agency. From the graphical analysis, it is noticeable that at aggregate (regional) level, the concentrations are affected by significant decreasing trends, but at a rather contained speed. This is particularly true for the concentrations of oxides (NO2 and NOX) in urban and industrial areas. However, particulate matters and ozone show a high persistence in the average concentrations, interrupted only by the alternation of climatic seasons. The data also show that the meteorology of the region does not seem favorable for the improvement of air quality, as the region is characterized by low precipitation, and wind almost everywhere is not very intense. This situation could be induced by the unfavorable geography of the area, which prevents adequate air recycling and facilitates the stagnation of pollutants. We suggest that any public policy intervention aimed at improving the air quality situation in the region should take into account this empirical evidence in the impact assessment phase.

Dynamic analysis of particulate pollution in haze in Harbin city, Northeast China

Abstract Based on the air quality data of Harbin in winter from 2015 to 2017, the national winter straw combustion data from 2016 to 2017 and the mixed single particle Lagrange comprehensive track model, Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT), dynamic analysis of Harbin’s winter air quality status and influencing factors. The air quality data were analyzed; it was found that the main pollutants in winter in Harbin were SO2, NO2, PM10 (particles with aerodynamic diameter ≤10 µm), and PM2.5 (particles with aerodynamic diameter ≤2.5 µm); the annual air pollution situation deteriorated sharply from November and continued until March of the following year. Through the research on straw fire prevention points in Harbin, the spatial pattern characteristics and causes of persistent haze in Harbin from 2015 to 2017 were dynamically analyzed. Combining the backward trajectory model to trace the source and trend of air mass in pollution, it is found that the air mass trend is consistent with the distribution of straw-burning points. The research results show that (1) during the winter from 2015 to 2017, the overall air quality situation in Harbin improved, the number of serious pollution days decreased year by year, and the main atmospheric pollutants were PM10 and PM2.5. In October and November, the pollution concentration peaked, and after December, the pollution concentration showed a downward trend until the next spring reached the valley and (2) the most obvious time of haze in Harbin is from November to December, and it is concluded that haze events are closely related to the large number of pollutants caused by the burning of straw around Harbin, and because the northwest monsoon climate affects the air quality, the transportation of fine particles caused by the burning of straw in winter in the surrounding areas of Harbin is the main cause of serious pollution in Harbin.

Source apportionment of ambient fine and coarse aerosols in Embalenhle and Kinross, South Africa

The South African Highveld is recognised as a region having significant negative ambient air quality impacts with its declaration as an Air Quality Priority Area in 2007. Such areas require the implementation of specific air quality intervention strategies to address the air quality situation. A greater understanding of the composition of the atmospheric aerosol loading and the contributing air pollution sources will assist with the formulation and implementation of these strategies. This study aims to assess the composition and sources of the aerosol loading in Embalenhle and Kinross located on the Highveld. Fine (PM2.5) and coarse (PM2.5-10) aerosol samples were collected during summer and winter, which were quantified using the gravimetric method. Wavelength-Dispersive X-Ray Fluorescence (WD-XRF) and Ion Chromatography (IC) analysis were used to determine the chemical composition of aerosols. Mean PM2.5 concentrations in Embalenhle and Kinross ranged from 16.3 to 34.1 µg/m3 during winter and 7.4 to 19.0 µg/m3 during summer. Mean PM10-2.5 concentrations ranged from 10.3 to 114 µg/m3 during winter and 5.9 to 11.2 µg/m3 during summer. Si, Al, S, Na (winter only), Ca (summer only), SO42- and NH4+ were the most abundant species in PM2.5 during both seasons. In PM10-2.5, Si, Al, Na (winter only), SO42- and F- were the most abundant species during both seasons. The elements S and Ca also had high abundances at Embalenhle and Kinross, respectively, during summer. Source apportionment was undertaken using Positive Matrix Factorisation, which identified five sources. Dust, secondary aerosols, domestic combustion, wood and biomass burning, and industry were determined to be the contributing sources. Any measures to mitigate particulate air pollution on the Highveld should consider these key sources.

Hybrid Data Mining Forecasting System Based on Multi-Objective Optimization and Selection Model for Air pollutants

The air quality index (AQI) indicates the short-term air quality situation and changing trend of the city, which includes six air pollutants: PM2.5, PM10, CO, NO2, SO2 and O3. Due to the diversity of pollutants and the fluctuation of single pollutant time series, it is a challenging task to find out the main pollutants and establish an accurate forecasting system in a city. Previous studies primarily focused on enhancing either forecasting accuracy or stability and failed to analyze different air pollutants at length, leading to unsatisfactory results. In this study, a model selection forecasting system is proposed that consists of data mining, data analysis, model selection, and multi-objective optimized modules and effectively solves the problems of air pollutants monitoring. The proposed system employed fuzzy C-means cluster algorithm to analyze 13 original AQI series, and fuzzy comprehensive evaluation is used to find out the main air pollutants in each city. And then multiple artificial neural networks are used to forecast the main air pollutants for each category and find the optimal models. Finally, the modified multi-objective optimization algorithm is used to optimize the parameters of optimal models and model selection to obtain final forecasting values from optimal hybrid models. The experiment results of datasets from 13 cities in the Beijing–Tianjin–Hebei Urban Agglomeration demonstrated that the proposed system can simultaneously obtain efficient and reliable data for air quality monitoring.

What drive carbon price dynamics in China?

This paper examines the driving factors of China carbon price in a systemic way with quantitative analysis. Specifically, three categories of driving forces as macro economy risk and uncertainty, energy and environment factors are selected to investigate their impacts on carbon price in China, by adopting the dynamic connectedness measurement approach. The empirical results confirm each driving factor has made contributions to the carbon price fluctuations, and the dynamic interactions among them have enhanced connectedness of the whole system. However, the directional dynamic spillovers indicate main driving factors to carbon price are heterogeneous varying over the whole sample and in different carbon markets. Most importantly, market sentiment plays main role in the carbon price dynamics of Guangdong market, whereas the electric power index makes great effects to Hubei carbon price changes, and the carbon price fluctuations in Shenzhen market are largely caused by air quality situation.

An Overview of Experiences Made and Tools Used to Inform the Public on Ambient Air Quality

Legislative regulations on atmospheric pollution have been established in different parts of the world for addressing air quality management. An important public commitment, common among all nations, is to ensure environmental safety and health protection, particularly for the most fragile population groups. Each country has its own rules and practices to provide adequate and timely information on ambient air quality. Information is given either through easily accessible media, including websites and apps, or by traditional means of telecommunication. An air quality index (AQI) is definitely a valuable tool for disseminating data on the main regulated pollutants and represents a readable indicator of the prevailing situation of air quality in the area. Several calculating expressions were formulated to combine, in a unique value, different parameters, and a few methods were created to determine and compare different AQIs. This paper gives almost a global overview of approaches and tools used to inform the public about the status of the ambient air quality. Different AQIs are analyzed to contribute to the sharing of air quality management practices and information to raise public awareness and to help policymakers to act accordingly.

Determination of air quality index and its impacts on human health in Chennai City

Air pollution in India particularly in metropolitan cities is a serious issue mainly due to vehicular emission, traffic congestion, burning of coal, fuelwood process. The objective of this project is to provide insightful details about the current situation of air quality across five locations in Chennai city and its impacts using an air quality detector meter. The concentration of pollutants namely Carbon dioxide (CO2), Formaldehyde (HCHO), Suspended Particulate Matter (SPM), Total Volatile Organic Compounds (TVOC) and, meteorological conditions like Temperature and Humidity are measured at five polluted areas in Chennai metropolitan city and Air Quality Index values are calculated. From the above Air Quality Index values, it is found that Marina Beach bus stop, Velachery bus stop, Mount Road government estate metro station, Koyambedu bus station, Alandur metro station are identified as problematic zones and health impacts are Published.

DESIGN AND RESULTS OF AN AI-BASED FORECASTING OF AIR POLLUTANTS FOR SMART CITIES

Abstract. This paper presents the design and the results of a novel approach to predict air pollutants in urban environments. The objective is to create an artificial intelligence (AI)-based system to support planning actors in taking effective and adequate short-term measures against unfavourable air quality situations. In general, air quality in European cities has improved over the past decades. Nevertheless, reductions of the air pollutants particulate matter (PM), nitrogen dioxide (NO2) and ground-level ozone (O3), in particular, are essential to ensure the quality of life and a healthy life in cities. To forecast these air pollutants for the next 48 hours, a sequence-to-sequence encoder-decoder model with a recurrent neural network (RNN) was implemented. The model was trained with historic in situ air pollutant measurements, traffic and meteorological data. An evaluation of the prediction results against historical data shows high accordance with in situ measurements and implicate the system’s applicability and its great potential for high quality forecasts of air pollutants in urban environments by including real time weather forecast data.

Developing a wavelet-AI hybrid model for short- and long-term predictions of the pollutant concentration of particulate matter10

Prediction of the particle concentration has received a great deal of research interest in recent decades, especially in the cities exposed to dust storms; hence, the use of early warning systems based on more accurate predictions to inform about the air quality situation in the next hours is very important. Thus, this study was conducted to improve the prediction accuracy of the particulate matter of aerodynamic diameter ≤ 2.5-μm particle concentration in the short term and long term, by combining the discrete wavelet transform technique with the artificial intelligence methods, i.e., adaptive neuro-fuzzy inference system, support vector regression, and artificial neural network. The data of the concentration of the suspended particles in Yazd city regarding 16 influential parameters were used during 2015–2019 in 6 models with and without wavelet, and the results obtained from these models were assessed and compared for hourly (short-term) and daily (long-term) timescales. The results based on 6 evaluation and validation criteria showed that the wavelet-support vector regression and wavelet-adaptive neuro-fuzzy inference system models have higher accuracy than other models in daily forecasting with an accuracy of about 99% and wavelet-support vector regression has higher accuracy in hourly forecasting with an accuracy of about 96%. The models combined with the wavelet transform produce better results rather than the single models showing the high effect of wavelet transformation in increasing the accuracy of artificial intelligence models used for prediction of the particulate matter ≤ 10 μm concentration.

Neural-Based Ensembles for Particulate Matter Forecasting

The air pollution caused by particulate matter (PM) has become a public health issue due to the risks to human life and the environment. The PM concentration in the air causes haze and affects the lungs and the heart, leading to reduced visibility, allergic reactions, pneumonia, asthma, cardiopulmonary diseases, lung cancer, and even death. In this context, the development of systems for monitoring, forecasting, and controlling emissions plays an important role. The literature about forecasting systems based on Artificial Neural Networks (ANNs) ensembles has been highlighted regarding statistical accuracy and efficiency. In this article, trainable and non-trainable combination methods are used for PM10 and PM2.5 (particles with an aerodynamic diameter less than 10 and 2.5 micrometers, respectively) time series forecasting for eight different locations, in Finland and Brazil, for different periods. Trainable ensembles based on ANNs, linear regression, and Copulas are compared with non-trainable combinations (mean and median), single ANNs, and linear statistical approaches. Different models are considered so far, including Autoregressive model (AR), Autoregressive and Moving Average Model (ARMA), Infinite Impulse Response Filters (IIR), Multilayer Perceptron (MLP), Radial Basis Function Networks (RBF), Extreme Learning Machines (ELM), Echo State Networks (ESN), and Adaptive Network Fuzzy Inference System (ANFIS). The use of ANNs ensembles, mainly combined with MLP, leads to a better one step ahead forecasting performance. The use of robust air pollution forecasting tools is prime to assist governments in managing air pollution issues like hospital collapse during adverse air quality situations. In this sense, our study is indirectly related to the following United Nations sustainable development goals: SDG 3 - good health and well-being and SDG 11 - sustainable cities and communities.