Air Pollution Problems Research Articles

The Emission Balance of Selected Groups of Fuels Used in Households to Generate Pollution in the Małopolskie Voivodeship

This article presents an analysis of the relationship between air pollution and the type of fuel used in households in the Małopolskie Voivodeship from 2010 to 2021. For this article, they are marked as HEU-C (coal), HEU-L (liquid), and HEU-O (other). The analysis area was selected due to the constantly occurring problem of air pollution with PM10 and PM2.5. Using regression, the relationships between energy consumption in households and specific components of air pollution recorded in the Małopolskie region were established. The developed models were used to determine the potential of individual groups of fuels to generate pollution. The primary finding suggests that the derived regression models offer an appropriate predictive framework. Studies show significant reductions in pollutants like BbF, Cd, Pb, and PM2.5. Increasing the use of HEU-O fuel is key to cleaner air in the Małopolskie Voivodeship. However, HEU-O may emit more NOx and NO2 than HEU-C. The selected thematic emphasis differentiates this article from others examining air quality issues within the Małopolskie Voivodeship.

Relationship Between Air Pollution and the Incidence of Selected Disease Among Children and Adolescents in the Years 2017-2023 in Selected Cities in Poland

The aim of this research is to examine the relationship between average annual air pollution concentrations in selected cities and the incidence of specific diseases among children and adolescents aged 0 to 18 in selected Polish cities - Gdańsk, Katowice, Poznań, and Lublin with varying levels of average annual air pollution [suspended particulate matter PM2.5 and PM10, benzo(a)pyrene, nitrogen dioxide]. The relationship between the incidence of selected diseases among children and adolescents (asthma, hypertension, food and skin allergies) from 2018 to 2023 and the quality of atmospheric air was examined. In Katowice, due to the presence of a developed industry, the permissible average annual concentrations of the analyzed air pollutants were exceeded in the initial period, unlike in Gdańsk and Lublin. However, the greatest challenge in Poland concerning air quality is the average annual concentration of benzo(a)pyrene. Nonetheless, there has been a significant decrease in recent years. Polish cities facing air pollution problems are undertaking various measures to improve air quality. Studies have shown significant connections between the concentrations of selected pollutants and the frequency of diseases such as bronchial asthma, hypertension, food and skin allergies. However, this impact varies from city to city.

Analyzing and forecasting air pollution concentration in the capital and Southern Thailand using a lag-dependent Gaussian process model.

The air pollution problem has now amassed worldwide attention due to its multifaceted harm to human health. Exploring the concentration of air pollution and improving forecast have important consideration worldwide. In this research, we analyze the air pollution concentration of Southern Thailand and compare it with the central region. Also, we proposed a methodology based on the lag-dependent Gaussian process (LDGP), a Bayesian non-parametric machine learning model, with a stable optimization approach, which is a cluster-based multi-starter technique based on the Nelder-Mead optimizer. This model also provides the confidence band for forecasted values. We also used autoregressive deep neural network (AR-DNN), autoregressive random forest (AR-RF), gradient boosting (GB), and K-nearest neighbors (KNN) models. A comparison of the proposed methodology was performed on the daily air pollution data collected from the southern provinces and also from the capital of Thailand from 1 January 2018 to 31 December 2022. We used well-established performance evaluation measures to compare the performance of the models. To evaluate the bias due to overfit, we performed a tenfold cross-validation for all the pollutants in each region and compared the models to choose the best one. Moreover, we explored the concentration of air pollution in these regions. Results of descriptive analysis revealed that Bangkok had a much higher concentration of air pollution as compared to the southern region. However, the southern region had higher exposure to PM air pollutants as per WHO recommendations and also had higher exposure to O3 and CO levels. The proposed LDGP model outperformed the other machine learning models for forecasting all air pollutants. Hence, it is recommended to be used by experts for further research and studies with different kernel functions. This research is also expected to contribute to local government planning and prevention and worldwide use of the same methodology for the sustainability of public health.

Estimation methods for the variance of Birnbaum-Saunders distribution containing zero values with application to wind speed data in Thailand.

Thailand is currently grappling with a severe problem of air pollution, especially from small particulate matter (PM), which poses considerable threats to public health. The speed of the wind is pivotal in spreading these harmful particles across the atmosphere. Given the inherently unpredictable wind speed behavior, our focus lies in establishing the confidence interval (CI) for the variance of wind speed data. To achieve this, we will employ the delta-Birnbaum-Saunders (delta-BirSau) distribution. This statistical model allows for analyzing wind speed data and offers valuable insights into its variability and potential implications for air quality. The intervals are derived from ten different methods: generalized confidence interval (GCI), bootstrap confidence interval (BCI), generalized fiducial confidence interval (GFCI), and normal approximation (NA). Specifically, we apply GCI, BCI, and GFCI while considering the estimation of the proportion of zeros using the variance stabilized transformation (VST), Wilson, and Hannig methods. To evaluate the performance of these methods, we conduct a simulation study using Monte Carlo simulations in the R statistical software. The study assesses the coverage probabilities and average widths of the proposed confidence intervals. The simulation results reveal that GFCI based on the Wilson method is optimal for small sample sizes, GFCI based on the Hannig method excels for medium sample sizes, and GFCI based on the VST method stands out for large sample sizes. To further validate the practical application of these methods, we employ daily wind speed data from an industrial area in Prachin Buri and Rayong provinces, Thailand.

Cost of escaping air pollution: A way to prevent excessive expansion of industrial areas

To pursue economic growth, many emerging markets prioritize providing land and resources to industries rather than people, resulting in people being forced to relocate. This paper uses Taiwan’s largest industrial city (Kaohsiung) as an example to discuss the negative spillover effect brought about by its most polluted industrial area (the Kaohsiung Linhai Industrial Park, KLIP). This study finds that the proximity between residences and the pollution sources and air pollution severity both cause housing prices to decrease. The results also show that among different air pollution indicators, SO2 concentration exerts the largest influence on housing prices. This may be because the SO2 emitted by large coal-fired power plants and oil refineries in the KLIP is linked to pollution-based haze and creates a visible atmospheric brown haze. By discussing the industrial park’s air pollution problem, this paper illustrates the harm of the overdevelopment of an industrial park. To prevent people’s relocation events resulting from the overdevelopment of an industrial park occurring in the future, we must regularly estimate the shadow price that residents living near the industrial park are required to pay to escape air pollution. This paper also puts forward suggestions that contribute to the sustainable development of industrial parks.

Air Pollution and Urban Economic Growth: A Study Based on Dynamic Models

<p class="Paragraph"><span lang="EN-US">With China's economic growth, the issue of air pollution has become increasingly prominent. As the core of political and economic development, 184 major cities in China face severe air pollution and significant environmental pressures due to high population density. Consequently, China has established key urban environmental monitoring stations in these cities to monitor pollution emissions in real time. This paper utilizes panel data from 184 Chinese cities spanning 2012-2022 and employs dynamic GMM models, fixed effects models, and mediation effect models to empirically examine the impact of air pollution on urban environmental pollution and its mediation mechanisms. From the perspective of cities with different income levels, a dynamic GMM model is established to determine the correlation between air pollution and various factors influencing urban economic growth. The study finds that air pollution significantly reduces economic growth in Chinese cities at the 1 per cent level, with each unit increase in air pollution reducing economic growth in Chinese cities by 0.84 units. Urbanization and human capital are identified as two crucial channels through which air pollution affects the economic development of Chinese cities. The negative impact of air pollution on economic growth is more pronounced in larger cities compared to smaller ones, and the adverse effects of haze pollution become increasingly significant over time. Therefore, policymakers should take the cultivation and application of new quality productivity as a key strategy to enhance the quality of economic development, and realise a win-win situation between economic growth and environmental protection through scientific and technological innovation and industrial restructuring. This will not only help alleviate the air pollution problem, but also inject new momentum into the high-quality development of China’s economy.</span></p>

“A Review Of The Study Of Estimation Of The Concentration Of Particulate Matter Over Chhatrapati Sambhajinager (M.S) City Using Remote Sensing”

India’s ongoing population explosion has imposed a great strain on the country’s environment. This rapid growth in population along with urbanization and industrialization has placed lot of pressure on India’s infrastructure and natural sources. Forests destruction, soil erosion, water pollution and lowering of the level’s quality continue to hinder economic development in under developed and rural region of India. The rapid urbanization and subsequently industrialization in India leads degrading environmental condition in metro cities in India. Chhatrapati Sambhajinager cannot be an exception Chhatrapati Sambhajinager is one of the most affected cities where air particulate matters (PM) have been registered at the levels more than 10 times our country’s legal limit (Ninawe et-al 2010). Despite of introducing legal policy measures in India air pollution remained major concern. In many urban areas including Chhatrapati Sambhajinager, there is high level of pollution. This cause increase in the levels of air pollution including deforestation that is causing human health hazards. In India aggravation of air pollution is owing to the rapid and growth in the size of cities. The movement of people from one place to other helped to Increase in consumption pattern, unplanned urban and industrial development is the main causes of air pollution in big cities of India that resulted the worst. (Chitale et-al 2010). In India, the most severe problem of environment comes in varied forms including vehicular emissions and unsaturated industrial smoke. This has resulted in bringing about adverse effect on population and cities are unable to implement pertinent pollution control mechanism. High concentration of pollution in India is not due to absence of a sound environment legal regime, but is due to lack of environmental enforcement at the local level. Regulatory reforms aimed to improving the air pollution problem in most of the metro cities. To my knowledge this might be the first attempt in Maharashtra, to produce high-resolution of particulate matter and gaseous NO2 and SO2 pollution surface, making use of remote sensing data (Kourase et-al 2010). The outcome of this research work will be made available to help expose health research in this polluted and densely populated region

Classification of Air Pollutant Index on Data with Outliers and Imbalance Class Problem

The problem of air pollution has become a global issue that has received attention from various countries. Jakarta, Indonesia's capital city, is unavoidable from the same problem. This study will use four parameters of substances PM10, SO2, CO, O3, and nitrogen dioxide to categorize Jakarta's air quality (NO2). The data used is daily data taken from the Air Quality Monitoring Station in Jakarta throughout 2020. The methods used include SVM, Random Forest, Logistic Regression, KNN, CART, and Stacking Algorithm. At the data preparation stage, we found missing values, outliers, and class imbalance problems. Before applying machine learning methods and evaluating accuracy, we used data pre-processing techniques such as the MissForest method, median substitution, and ADASYN. The results prove that the original dataset has a higher accuracy score (0.882 – 0.977) than the balanced dataset (0.829 – 0.976). According to the evaluation results, the Random Forest method has the highest accuracy score for original and balanced datasets. The overall result is better than the identical research, which produces 96.61% accuracy using a neural network. It shows that preprocessing steps such as missing values handling an imbalanced class handling is essential in classification performance.

Impact of Urbanization on Air Quality in Largest Сities of Kazakhstan

In the context of the rapid growth of urbanization and industrialization of Kazakhstan, the air pollution problem in large cities is becoming increasingly urgent. It is essential to assess the impact of economic growth on the environmental situation, as this allows us to develop strategies to improve air quality and ensure sustainable development. Thus, this study examines the impact of urbanization and economic growth on air pollution across four major cities in Kazakhstan from 2016 to 2022. Statistical methods were used to analyze the relationship between pollution levels and economic indicators, including gross regional product (GRP) and industrialization. The study covers the period from 2016 to 2022 and is based on data on concentrations of critical pollutants collected from the National Bureau of Statistics of the Republic of Kazakhstan. National monitoring sources collected air quality data, including PM2.5, PM10, SO2, NO2, and CO concentrations. Statistical analyses, as correlation and regression models, were applied to establish relationships between pollution levels and economic growth indicators. The study found significant correlations between economic activities and air pollution levels. In Almaty, a 10% increase in GRP corresponds to a 5% increase in pollutant concentrations. Almaty experienced the highest average PM2.5 concentration at 42 µg/m³, exceeding permissible norms by 20%. Other cities showed varied levels of pollution influenced by specific economic and industrial profiles. The results underscore the pressing need for effective urban management and strategic policy-making to mitigate the adverse effects of economic development on air quality.

Time Series Forecasting of Air Quality Index in Lahore : A Machine Learning Perspective with Facebook Prophet Model

This research investigates the rising problem of air pollution, which is a serious issue affecting people's health and the environment on a global scale. Focusing on Lahore, Pakistan, a city facing severe pollution, the research examines the Air Quality Index (AQI) from May 2019 to December 2023. Using the Facebook Prophet model for time-series forecasting, the analysis reveals trends at monthly, weekly, and hourly intervals. Peak AQI concentrations are identified in winter months, notably December and January, aligning with hazardous smog levels. Weekly trends show minimum AQI on weekends, particularly Saturday and Sunday. The hourly trend points to peak AQI concentrations between 7:00 and 09:00 AM, correlating with morning traffic and emphasizing the role of vehicular emissions in air quality degradation. These forecasts are crucial for anticipating and mitigating the adverse impact of air pollution on human health. This research underscores the critical need for comprehensive strategies to address air pollution, acknowledging the Lahore High Court and Government's initiatives, such as artificial rain experimentation, closure of commercial markets by 10 PM, ban on cutting trees and smoke-emitting vehicles. In summary, this research significantly contributes to understanding and tackling the increasingly urgent challenges associated with air quality.

Bayesian dynamic models to estimate the impact of halting vehicle fleets on the air quality: a case study from Medellín, Colombia

According to the World Health Organization (WHO, Ambient (outdoor) air pollution (who), 2020), outdoor air pollution is estimated to have caused 4.2 million premature deaths worldwide in 2019. Under this scenario, the development of statistical tools for assessing the impact of a possible reduction of fossil fuels-based transportation systems on air quality is more relevant than ever. This type of instruments can help policy makers to take the right path to tackle the problem of air pollution in urban environments. Thus, this work proposes a method to evaluate the impact of the reduction of vehicle fleets on air quality. The method is based on the construction of counterfactual time series, which represent observed air pollutants under a treatment of interest. In this case, the treatment of interest are the vehicle fleets on the streets. The construction of the counterfactual time series is based on Bayesian dynamic linear models. Several impact assessment measures are implemented, taking advantage of the counterfactual and observed time series (the observed pollutant without the treatment). The flexibility of the Bayesian approach allows easy inference of the impact quantities via high posterior density intervals. Some simulation analyses show that the method works well under different impact scenarios, and one application regarding Medellin’s case in Colombia is presented. For the application, it is shown that the drastic reduction in vehicle fleets during the COVID-19 mobility restrictions led to a significant reduction in the presence of different pollutants (PM10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {PM}_{{10}}$$\\end{document}, PM2.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {PM}_{2.5}$$\\end{document}, NO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {NO}_{{2}}$$\\end{document}, NOx\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {NO}_{{x}}$$\\end{document})

Assessing the Variability of Aerosol Optical Depth Over India in Response to Future Scenarios: Implications for Carbonaceous Aerosols

AbstractAir pollution caused by various anthropogenic activities and biomass burning continues to be a major problem in India. To assess the effectiveness of current air pollution mitigation measures, we used a 3D global chemical transport model to analyze the projected optical depth of carbonaceous aerosol (AOD) in India under representative concentration pathways (RCP) 4.5 and 8.5 over the period 2000–2100. Our results show a decrease in future emissions, leading to a decrease in modeled AOD under both RCPs after 2030. The RCP4.5 scenario shows a 48%–65% decrease in AOD by the end of the century, with the Indo‐Gangetic Plain (IGP) experiencing a maximum change of 25% by 2030 compared to 2010. Conversely, RCP8.5 showed an increase in AOD of 29% by 2050 and did not indicate a significant decrease by the end of the century. Our study also highlights that it is likely to take three decades for current policies to be effective for regions heavily polluted by exposure to carbonaceous aerosols, such as the IGP and eastern India. We emphasize the importance of assessing the effectiveness of current policies and highlight the need for continued efforts to address the problem of air pollution from carbonaceous aerosols, both from anthropogenic sources and biomass burning, in India.

Analyzing Research on Air Quality Modeling in the Indian Setting: A Thorough Overview

India, being a developing nation, need efficient strategies to mitigate air pollution in order to prevent premature deaths of numerous individuals. Air quality models not only give data on the concentrations of air pollution, but also offer valuable knowledge about its sources. Prior research on air quality modelling conducted in India at the local and regional levels. This present study aims to evaluate the comprehensive grasp of the existing gaps and to explore potential future possibilities. The meticulously recorded studies conducted in various regions of India during the previous decade, employing systematic searches on various databases like Google Scholar and Google. The majority of air quality research mostly centers on megacities, disregarding the smaller cities that also require substantial attention in the future. There were very few research that were primarily focused on the central area of India, even though the majority of modelling studies were conducted in that region. Upon reviewing both local and regional numerical models, it became evident that there is a requirement for improved emission inputs. Furthermore, the statistical models have shown that it is important to meticulously choose key indicators in order to achieve precise source identification. Regardless of the emission inventory and models employed, Delhi consistently has significant underestimation of particulate matter concentrations, exacerbating its severe air pollution problems. The primary contributors to particulate matter in India are dust and emissions from transportation.

A study on the influence of utilizing hydrogen at fuelling spark ignition engines

Abstract In the last 30 years, remarkable progress has been made in reducing pollutant emissions and fuel consumption in conventional internal combustion engines. Active and passive methods of reducing exhaust emissions have caused them to decrease significantly over the level of the 1980s. However, the problems of air pollution and the greenhouse effect are far from being solved. International organizations and institutions are pushing for measures to reduce emissions, especially the implementation of “zero-emission” (ZEV) vehicles. Due to its excellent combustion properties and the very low pollutant emissions resulting from its combustion, hydrogen is seen as an alternative fuel of the future. Hydrogen can be used as a fuel in two ways: in internal combustion piston engines or in fuel cells. Research into the use of hydrogen as a fuel for spark ignition engines is divided into two directions: additive fuel or a total gasoline substitution with hydrogen. Compared to the gasoline-powered engine, the fully hydrogen-powered engine runs more economically at low loads but it can create a decrease of performance at high loads due to the high volumes of space needed in the combustion chamber. Because of the very low density, high volumes of hydrogen are difficult to storage, requiring also high pressure, so running only on hydrogen wouldn’t be practical. Combining the advantages of the two fuels came the use of gasoline with addition of hydrogen. An important aspect is that emissions are significantly reduced due to the very good combustion properties of hydrogen and the high diffusivity that helps to form a homogeneous mixture. This paper presents aspects of fuelling a spark ignition engine with gasoline and hydrogen. The influence of hydrogen on the cylinder mixture, on the energy and emissions of the engine is analysed. We will focus on: engine power, fuel consumption, the level of pollutant emissions and greenhouse gases, at the studied regimes.

Air Quality Awareness—Empirical Evidence from a Comparative Perspective between Two European Cities

This research, conducted as part of the European project PA-MAP (Participatory Approach to Monitoring Air Quality in Urban Environments), investigates citizens’ perceptions of air pollution in two European cities: Turin (Italy) and Dublin (Ireland). We systematically collected social data through semi-structured surveys distributed via online platforms. Our aim was to gather comprehensive information on the perception of pollution sources and the effectiveness of control systems. We examined citizens’ habits and their suggestions to improve air quality. The results revealed the following: (1) In Turin, the perception of air pollution significantly influences individuals’ self-assessments of health. Consequently, increased vigilance is required to mitigate environmental health risks and to disseminate information on air quality and effective practices for reducing air pollution. (2) The use of sustainable transportation in Turin is hindered due to the inadequate maintenance of cycling infrastructure. Citizens prefer to use cars or trains, which facilitate rapid travel. (3) In Dublin, greater sensitivity to the problem of air pollution has emerged, and the use of sustainable means of transport is facilitated via ad hoc infrastructure. These results provide empirical evidence to policymakers to support the promotion of concrete local actions through which to improve air quality.

ENVIRONMENTAL AND HEALTH EFFECTS OF AIR POLLUTION AND THE SOLUTIONS (A CASE STUDY OF AWKA NORTH LOCAL GOVERNMENT AREA OF ANAMBRA STATE, NIGERIA).

Air pollution has long been a major global concern in developing countries, posing serious health and environmental challenges. The awareness of the effects of air pollution on human health and the environment and various ways to mitigate the impact of air pollution was investigated in Awka, Anambra State. The population size was 1000. In carrying out this research, three hypotheses were postulated and tested at a 5% level of agreement. A well-structured questionnaire was used to collect data from the subjects, and simple percentages, and weighted mean statistics were used to analyze the data. In contrast, the Chi-square was used in testing the hypotheses. Results reveal that over 50 % of the residents of Awka are fully aware of the air pollution problem in the town and are knowledgeable about the dangers of air pollution. Air pollution is prevalent in Awka town, which is largely caused by heavy traffic resulting in increased vehicular emissions of poisonous gases. The findings show that air pollution significantly negatively impacts human health and the environment. The study recommends awareness creation, enactment of suitable legislation, and activities that promote air pollution, such as open incineration of wastes and bush burning, especially for agricultural purposes, should be discouraged. We underscore the necessity of facilitating more research on the associations between exposure and health in marginalized communities that are disproportionately affected by air pollution yet inadequately represented in existing investigations.

Research on the process of preparing fiber filtration membrane by centrifugal blowing electrospinning technology

AbstractCurrently, the problem of air pollution is getting more and more serious, especially the pollution of tiny solid particles (PM, particulate matter) in the air, which poses a great challenge to the environment and human health. Therefore, it is particularly important to develop air filtration materials with high filtration efficiency and low resistance to meet this challenge. In this study, we propose to prepare air filtration membranes by centrifugal jet electrospinning technology, design multifactorial orthogonal coupling experiments, and investigate the effects of key process parameters of centrifugal jet electrostatic spinning, including solution concentration, airflow rate, rotational speed, and voltage, on the fiber diameter and uniformity by applying the polar analysis of variance and analysis of variance. PAN‐BaTiO3 electret nanofiber filtration membranes with high efficiency and low resistance were prepared by combining the optimized process of centrifugal blowing electrospinning. Various characterization methods (tensile test, water contact angle test, surface charge test, filtration test) were used to evaluate the effect of different BaTiO3 electret contents (0%, 0.1%, 0.5%, 1%) on the comprehensive performance of the filtration membranes, and the electret filtration efficiency of the electret filtration membranes prepared by the present process reached as high as 96.56% with a pressure drop of 29 Pa.

Impacts of Climatic Changes and Air Pollution on Public health and Environment

Air pollution is an emerging highlight in developing countries in changing climate, it is considered one of the greatest concerns of public, individual, and environmental health. The rate of mortality and morbidity seems to increase due to air contamination. There are many types of air pollutants, some of the major air pollutants are particulate matter (PM), carbon monoxide (CO), nitrogen oxides (NOx), sulfur oxides (SOx), and lead (Pb). PM are minute suspended particles that can inhaled while breathing and cause cardiovascular and respiratory disorders, nervous system diseases, and cancer. High levels of CO inhalation cause poisoning in humans. Nitrogen oxides are latent greenhouse gases with high global warming potential and deplete the ozone layer. SOx causes intoxication when absorbed by the human body. When lead is absorbed by the human body by inhalation it may cause toxicity, chronic obstructive pulmonary disease, asthma, and lung cancer. Thereafter air pollution and changing climate lead to the spread of many infectious diseases. Hence, the way to manage this problem of air pollution is to make the public aware of impacts and management practices. The scientific and medical experts should conduct multidisciplinary research at the national and international level to sort out this problem in a sustainable way.

Oxy-fuel co-combustion properties and N-containing pollutants release characteristics of biomass/coal blends

Thermal properties and NO release characteristics during oxy-fuel co-combustion of pine sawdust (PS) and Shenmu coal (SM) were investigated based on the effects of various parameters, including atmosphere, temperature, blending ratio, and oxygen concentration in this research. The results indicate that the release of NO decreased with the increasing temperature during decomposition process of PS and SM. The content of N-Q and N-X in semicoke (SC) increased at higher torrefaction temperature. The N-Q and N-X content for SC at the temperature of 1000 °C was 36.9 % and 23.2 %, respectively. When 40 % PS was added to the SM, the NO release amount was significantly reduced by 16.3–23.1 % in the oxy-fuel atmosphere compared to the O2/N2 atmosphere, and the ignition time was reduced from 4.0s to 0.66s. The NO emission initially increased and then decreased as the combustion temperature increased from 800 to 1000 °C. Furthermore, increasing the oxygen concentration from 10 % to 40 % shortens the combustion time and increases the emissions of NO. Conversely, increasing PS blending ratios from 10 % to 40 % decreased NO emission and the conversion ratio. These findings emphasize that adding biomass can effectively improve coal ignition, increase combustion rates, reduce NO emissions, and address the air pollution problems associated with NOx emissions.

Air Quality Prediction and Ranking Assessment Based on Bootstrap-XGBoost Algorithm and Ordinal Classification Models

Along with the rapid development of industries and the acceleration of urbanisation, the problem of air pollution is becoming more serious. Exploring the relevant factors affecting air quality and accurately predicting the air quality index are significant in improving the overall environmental quality and realising green economic development. Machine learning algorithms and statistical models have been widely used in air quality prediction and ranking assessment. In this paper, based on daily air quality data for the city of Xi’an, China, from 1 October 2022 to 30 September 2023, we construct support vector regression (SVR), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), random forests (RF), neural network (NN) and long short-term memory (LSTM) models to analyse the influence of the air quality index for Xi’an and to conduct comparative tests. The predicted values and 95% prediction intervals of the AQI for the next 15 days for Xi’an, China, are given based on the Bootstrap-XGBoost algorithm. Further, the ordinal logit regression and ordinal probit regression models are constructed to evaluate and accurately predict the AQI ranks of the data from 1 October 2023 to 15 October 2023 for Xi’an. Finally, this paper proposes some suggestions and policy measures based on the findings of this paper.