Air Pollution Management Research Articles

A Long Short-Term Memory Neural Network for Daily NO2 Concentration Forecasting

NO2 pollution has caused serious impact on people's production and life, and the management task is very difficult. Accurate prediction of NO2 concentration is of great significance for air pollution management. In this paper, a NO2 concentration prediction model based on long short-term memory neural network (LSTM) is constructed with daily NO2 concentration in Beijing as the prediction target and atmospheric pollutants and meteorological factors as the input indicators. Firstly, the parameters and architecture of the model are adjusted to obtain the optimal prediction model. Secondly, three different sets of input indicators are built on the basis of the optimal prediction model to enter the model learning. Finally, the impact of different input indicators on the accuracy of the model is judged. The results show that the LSTM model has high application value in NO2 concentration prediction. The maximum temperature and O3 among the three input indicators improve the prediction accuracy while the NO2 historical low-frequency data reduce the prediction accuracy.

Study on Monitoring Plan for Air Pollutant Management in Flare Stack in Korea

Study on Monitoring Plan for Air Pollutant Management in Flare Stack in Korea

GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms.

Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore, accurate prediction of air quality is crucial for mitigation planning to support urban sustainability and resilience. Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI). Incorporating PM in AQI studies is crucial because of its easily inhalable micro-size which has adverse impacts on ecology, environment, and human health. Accurate and timely prediction of the air quality index can ensure adequate intervention to aid air quality management. Therefore, this study undertakes a spatial hazard assessment of the air quality index using particulate matter with a diameter of 10 μm or lesser (PM10) in Selangor, Malaysia, by developing four machine learning models: eXtreme Gradient Boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), and Naive Bayes (NB). Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70% of the dataset, while 30% was used for cross-validation. Results showed that XGBoost has the highest overall accuracy and precision of 0.989 and 0.995, followed by random forest (0.989, 0.993), K-nearest neighbour (0.987, 0.984), and Naive Bayes (0.917, 0.922), respectively. The spatial air quality maps were generated by integrating the geographical information system (GIS) with the four MLAs, which correlated with Malaysia's air pollution index. The maps indicate that air quality in Selangor is satisfactory and posed no threats to health. Nevertheless, the two algorithms with the best performance (XGBoost and RF) indicate that a high percentage of the air quality is moderate. The study concludes that successful air pollution management policies such as green infrastructure practice, improvement of energy efficiency, and restrictions on heavy-duty vehicles can be adopted in Selangor and other Southeast Asian cities to prevent deterioration of air quality in the future.

Collaborative Governance for Management of Water and Air Pollution caused by Industrial Activities in Lamongan Regency, East Java

This study aimed to 1) analyze water and air pollution caused by industrial activities, 2) investigate water and air pollution management carried out by government agencies, 3) examine the Collaborative Governance process in controlling water and air pollution. This study used a qualitative method with a phenomenological approach. The data were collected using data collection techniques through in-depth interviews, field observations, and document studies. The results showed that 1) industrial activities in Lamongan Regency generated water pollution with a percentage of 17%, namely in Sidoharjo River and Dapur River, as well as a decrease in air quality due to many industries that did not adhere to air pollution control regulations, hence increasing CO2 gas emissions by (72% ) or 3,712,849.07 Tons/Year, 2) reporting water and air pollution management done by submitting the related environmental documents through the SILILA Application (Sistem Informasi Lingkungan Lamongan), checking environmental documents, monitoring control, and socialization, 3) the execution of Collaborative Governance process in controlling water and air pollution is through five stages which include 1) public reports, 2) dialogue, 3) monitoring, 4) follow-up by governance, and 5) actions.

Interaction of interregional O3 pollution using complex network analysis.

In order to improve the accuracy of air pollution management and promote the efficiency of coordinated inter-regional prevention and control, this study analyzes the interaction of O3 in Qilihe District, Lanzhou City, China. Data used for analysis was obtained from 63 air quality monitoring stations between November 2017 and October 2018. This paper uses complex network theory to describe the network structure characteristics of O3 pollution spatial correlation. On this basis, the node importance method is used to mine the sub-network with the highest spatial correlation in the O3 network, and use transfer entropy theory to analyse the interaction of pollutants between regions. The results show that the O3 area of Qilihe District, Lanzhou City can be divided into three parts: the urban street community type areas in urban areas, the township and village type areas in mountain areas and the scattered areas represented by isolated nodes. An analysis of the mutual influence of O3 between each area revealed that the impact of O3 on each monitoring station in adjacent areas will vary considerably. Therefore these areas cannot be governed as a whole, and the traditional extensive management measures based on administrative divisions cannot be used to replace all other regional governance measures. There is the need to develop a joint prevention and control mechanism tailored to local conditions in order to improve the accuracy and efficiency of O3 governance.

Development of a new emission reallocation method for industrial sources in China

Abstract. An accurate emission inventory is a crucial part of air pollution management and is essential for air quality modelling. One source in an emission inventory, an industrial source, has been known with high uncertainty in both location and magnitude in China. In this study, a new reallocation method based on blue-roof industrial buildings was developed to replace the conventional method of using population density for the Chinese emission development. The new method utilized the zoom level 14 satellite imagery (i.e. Google®) and processed it based on hue, saturation, and value (HSV) colour classification to derive new spatial surrogates for province-level reallocation, providing more realistic spatial patterns of industrial PM2.5 and NO2 emissions in China. The WRF-CMAQ-based PATH-2016 model system was then applied with the new processed industrial emission input in the MIX inventory to simulate air quality in the Greater Bay Area (GBA) area (formerly called Pearl River Delta, PRD). In the study, significant root mean square error (RMSE) improvement was observed in both summer and winter scenarios in 2015 when compared with the population-based approach. The average RMSE reductions (i.e. 75 stations) of PM2.5 and NO2 were found to be 11 µg m−3 and 3 ppb, respectively. Although the new method for allocating industrial sources did not perform as well as the point- and area-based industrial emissions obtained from the local bottom-up dataset, it still showed a large improvement over the existing population-based method. In conclusion, this research demonstrates that the blue-roof industrial allocation method can effectively identify scattered industrial sources in China and is capable of downscaling the industrial emissions from regional to local levels (i.e. 27 to 3 km resolution), overcoming the technical hurdle of ∼ 10 km resolution from the top-down or bottom-up emission approach under the unified framework of emission calculation.

Land use regression modelling of ambient PM2.5 air pollution in Accra, Ghana

BACKGROUND AND AIM: Ambient air pollution is a growing public health concern in sub-Saharan African (SSA) cities. Yet, effective air quality management is hampered by limited data and modelling of spatial trends. METHODS: We measured outdoor weekly gravimetric (filter-based) concentrations of PM2.5 at 146 locations in a city-wide measurement campaign from April 2019 to June 2020 comprising of 10 yearlong and 136 weeklong sites. We used these data together with geospatial predictors to develop land use regression (LUR) models to estimate log transformed PM2.5 concentrations across the Greater Accra Metropolitan Area, Ghana. Geospatial predictor variables included land use, road networks, locations of human activity and the airport, population and building density, vegetation, elevation, and temporal predictors included temperature, rainfall, relative humidity, and month of the year. We selected the final parsimonious model through a stepwise forward selection procedure. We examined model external predictive accuracy with 10-fold 10% random site hold-out cross-validation and computed the R2 and Mean Absolute Error (MAE). RESULTS:PM2.5 concentrations exceeded the WHO’s annual average guideline of 10 ug/m3 at all measurement sites ranging from 15 to 80 ug/m3. In the LUR models, predictor variables representing weather conditions, vegetation, and population density explained the most variation in PM2.5 concentrations with a cross-validation R2 of 0.79 and MAE of 0.28 log(ug/m3). Predicted PM2.5 concentrations were highest in Accra central, the port city of Tema to the east, and in the highest-density residential areas, including some of Accra’s slums. CONCLUSIONS:Considering that ambient PM2.5 concentrations surpassed WHO health-based guidelines almost everywhere in the GAMA, ambient air pollution management and control should be prioritized in the city. KEYWORDS: Air pollution, Particulate Matter, Environmental Disparities

RETRACTED ARTICLE: Research on coastal atmospheric change and Latin dance performance based on target detection network

In recent years, with the progress of industrialization, human demand for natural resources has increased, China’s environmental degradation, and the management and treatment of volatile organic pollutants, etc., will not only become the cause of ozone pollution, but also have a huge impact on human health. In response to this problem, China issued the “Twelfth Five-Year Plan on Air Pollution Prevention and Management in Major Regions” in 2012, which for the first time clearly pointed out the policy on the management of volatile organic pollutants and the strengthening of corporate production, and management of volatile organic pollutants during transportation. Increase the use of low-volatile organic solvents, reduce the use of high-volatile organic solvents, promote the management of organic waste gas pollution, and strengthen the recovery and utilization of organic waste gas. Establish a monitoring system for volatile organic pollutants in major industries and improve the emission standards for volatile organic pollutants. Latin dance is a dance sport introduced to China in 1980, and now China holds Latin dance competitions every year. From an aesthetic point of view, Latin dance is a kind of artistic performance. Skilled Latin dance dancers during the performance can bring wonderful beauty to the audience and stimulate people’s love for this dance movement. Based on the summary of the related content of Latin dance art performance, this article further analyzes the current situation and development trend of Latin dance art performance, hoping to provide some valuable reference materials to promote the development of Latin dance art in China. Through survey methods such as questionnaire surveys and expert interview methods, the artistic performance of Latin dance is investigated.

Three-Year Variations in Criteria Atmospheric Pollutants and Their Relationship with Rainwater Chemistry in Karst Urban Region, Southwest China

Air pollutants have been investigated in many studies, but the variations of atmospheric pollutants and their relationship with rainwater chemistry are not well studied. In the present study, the criteria atmospheric pollutants in nine monitoring stations and rainwater chemistry were analyzed in karst Guiyang city, since the time when the Chinese Ambient Air Quality Standards (CAAQS, third revision) were published. Based on the three-year daily concentration dataset of SO2, NO2, CO, PM10 and PM2.5, although most of air pollutant concentrations were within the limit of CAAQS III-Grade II standard, the significant spatial variations and relatively heavy pollution were found in downtown Guiyang. Temporally, the average concentrations of almost all air pollutants (except for CO) decreased during three years at all stations. Ratios of PM2.5/PM10 in non- and episode days reflected the different contributions of fine and coarse particles on particulate matter in Guiyang, which was influenced by the potential meteorological factors and source variations. According to the individual air quality index (IAQI), the seasonal variations of air quality level were observed, that is, IAQI values of air pollutants were higher in winter (worst air quality) and lower in summer (best air quality) due to seasonal variations in emission sources. The unique IAQI variations were found during the Chinese Spring Festival. Air pollutant concentrations are also influenced by meteorological parameters, in particular, the rainfall amount. The air pollutants are well scoured by the rainfall process and can significantly affect rainwater chemistry, such as SO42−, NO3−, Mg2+, and Ca2+, which further alters the acidification/alkalization trend of rainwater. The equivalent ratios of rainwater SO42−/NO3− and Mg2+/Ca2+ indicated the significant contribution of fixed emission sources (e.g., coal combustion) and carbonate weathering-influenced particulate matter on rainwater chemistry. These findings provide scientific support for air pollution management and rainwater chemistry-related environmental issues.

Multi-output TCN autoencoder for long-term pollution forecasting for multiple sites

Air pollution is one of the major environmental issues attracting massive attention from researchers and policymakers. Both the developed and developing countries are undergoing a high concentration of pollution levels. Fine particulate matter PM2.5 (particles having a diameter less than 2.5μm) and PM10 (particles having a diameter less than 10μm) can easily penetrate the lungs and respiratory system and causes adverse health issues like heart attacks, cardiovascular diseases, lung function reduction. Real-time pollutant information is of great importance to providing prompt and complete information on air quality. Air pollution forecasting is another significant step of air pollution management, which can help policymakers and citizens make proper decisions to prevent air pollution-related diseases. This research study explores a novel pollutant forecasting model named as Multi-output temporal convolutional network autoencoder (MO-TCNA). The model accumulates each step's predicted values to perform multi-step ahead long-term forecasting for multiple pollutants and multiple sites in a single training model. The MO-TCNA network serves both the PM2.5 and PM10 pollutants forecasting for various locations instead of performing single output and site-specific pollutant forecasting. Consequently, the experimental results show that the MO-TCNA network is time-saving and has better performance than the traditional site-specific forecasting models.

The Harm and Prevention Management of Air Pollution in Environmental Engineering

The Harm and Prevention Management of Air Pollution in Environmental Engineering

Identification of air pollution hotspots in urban areas - An innovative approach using monitored concentrations data

Critical assessment of spatio-temporal variations in pollution levels is a crucial step for identifying and prioritizing air pollution hotspots (APH) in urban areas. There is no universally accepted methodology for defining and delineating air pollution hotspot which can be source-specific, pollutant-specific and time-specific. The present research article is an attempt to develop a protocol for identifying APH for any pollutant within a city where-in three criteria-based innovative methodology has been derived. The three criteria are frequency of exceedance (% of days), scale of exceedance and consistency in exceedance (consecutive number of days) to the specified standards that need to be met continuously for at least three years. The suggested methodology has been applied on a three-year database (2018–2021) of 37 continuous ambient air quality stations to identify PM2.5 specific APH. The analysis indicates 11 APH in April, 9 in May, 2 in June and almost the entire city during the October–February months. Given prioritization of implementation of control actions, the identified APH during summer has been further physically examined to map source activity types and their suitability for ambient air quality monitoring stations as per the guidelines. The APH can be the priority areas for the implementation of control actions by urban local bodies. The management of air pollution at these priority areas would be more effective instead of city-scale management practice, which is difficult to implement and monitor.

Particle-bound PAHs induced glucose metabolism disorders through HIF-1 pathway

Vehicle exhaust, as one of the most important compositions of air pollution, induced various adverse health effects, especially diabetes, on human beings. Even though monitoring and epidemiological data indicates that particle-bound polycyclic aromatic hydrocarbons (PAHs) is an inducing factor of diabetes, the specific causative mechanisms are still unclear. In the current study, the concentration of particulate matters (PMs, including PM1.0, PM2.5 and PM10.0) and PAHs was investigated at rush hour of weekday in three urban underground parking garages (UPGs). To evaluate the impacts of particle-bound PAHs on human beings, analysis of non-target metabolomics and unmetabolized PAHs were conducted for UPG and non-UPG worker urine samples. The results showed that the highest concentrations of PMs and total PAHs were found at the UPG entrance. The concentrations of unmetabolized 5-6 rings PAHs in the UPG worker urine were significantly higher than that in non-UPG worker urine samples, which induced glucose metabolism disorders through hypoxia-inducible factor 1 (HIF-1) signaling pathway. This could be a reason for particle-bound PAHs induced-diabetes on road workers, drivers and garage staff. These findings can serve as a step towards air pollution management and the pathological mechanism analysis of environmental factor induced-diseases.

Investigation of harbor craft activities for emission inventory calculation

ABSTRACT Total emissions from all sources need to be accurately quantified in an emission inventory in order for a basin to develop their air pollution management plan. The best approach is to measure both the real-world emissions and activity for a source; however, often only emissions are measured and activity is estimated from historical factors. This report focuses on harbor craft and presents data showing that using measured emissions and historical factors for activity will lead to inaccurate emission contributions. In this research, real-world activity data were collected from the engine control module (ECM) and from a surrogate method that relied on exhaust temperature. Measured real-world activity values from 10 harbor craft were compared with historical values found in the certification cycle. The vessels included four tugboats, three pilot boats, two police boats, and one supply vessel. The results showed the activity values used in the certification cycle did not reflect the real-world activity of the 10 harbor craft vessels. On average, real-world NOx emissions were only 46% of the NOx emissions estimated by ISO 8178-E3 weighting factor. In contrast, inclusion of significantly lower load factors leads to 31% higher in-use NOx emission factors on average. CARB/EPA load factor ranges from 0.45 to 0.51 while in-use load factor ranged from 0.14 to 0.44. From this finding, researchers are cautioned about accepting the activity values in a certification test cycle, like ISO 8178 E3, and instead should measure real world activity data. This change would improve the accuracy of the emission contribution from harbor craft to the local inventory. Implications: Real-world measurements of activity and emissions are the best way to get an accurate emission contribution to emission inventory. This paper reports on the differences between the use of the traditional certification cycle and real-world activity of harbor craft. Engine control module (ECM) and exhaust temperature data from 10 harbor crafts with different types of operation were used to compare real-world activity data to certification cycle.

High resolution aerosol optical depth retrieval over urban areas from Landsat-8 OLI images

The satellite-retrieved aerosol optical depth (AOD) provides unique estimation of aerosol loading across a continuous space. However, current AOD products with a relatively coarse resolution (≥1 km) can hardly capture the details in urban areas with large spatial AOD gradients. To address this issue, here we developed a novel retrieval algorithm for retrieving AOD with extra fine spatial resolution (30 m) from Landsat-8 satellite OLI images. In the algorithm, the three land surface reflectance (LSR) estimation schemes and four aerosol types are adopted to improve the retrieval accuracy. The algorithm is applied on Beijing and Wuhan city in China during 2014–2019. Results suggest that the retrieved AOD with the new algorithm exhibits good agreement with the ground-based measured AOD (R2 = 0.920), and 81.63% of the AODs fall within the expected error line with a root-mean-square error of 0.112. Moreover, the high-resolution AOD products also successfully identified polluted sources and discrepancy of aerosol loading over different land cover types in two megacities in China, implying its great potential on detecting fine aerosol emission sources over the complex urban area. Such improvement of the new algorithm for retrieving 30 m AOD developed in this study demonstrates its substantial potentials in supporting further studies of air pollution management and human exposure at extra-fine spatial scale.

항만 배후지역의 대기오염 실태로 본 항만환경 관리정책의 새로운 방향성

The ultimate purpose of this study is to explore the possibility of air pollution and damage to the hinterland that may be caused by the development and operation of the port, and to examine it by using especially analysis and optimization methods. The Busan New Port which is the largest trading port in Korea operating across Busan and Changwon (Gyeongnam) have been undertaken. Two field experiments on the concentration of coarse particulate matter (PM10, Hereafter PM10) and the level of influence of PM10 were investigated at two sites in the industrial and residential areas behind Busan New Port (Changwon). As the results, the concentration of PM10 was different for each site. In particularly, the actual air quality was exceeded the standard set as PM10 100㎍/㎥ for the case of the industrial area within the port area. Beside that, the observated values at the residential area did not exceed the standard, but were higher than other areas’s such as Noksan-dong and Gyeonghwa-dong. In order to effectively manage air pollution in the hinterland of ports, not only environmental and technical measures, but also cooperation with the private sector and local governments should be take into account.

Environmental impacts on the design and arts environment for industry 3.0 cotton roller ginning process

The past five decades have been characterised by passage of the environmental impact assessment (EIA) legislation dealing with the environment, including legislation on the control of land, air and water pollution, solid and hazardous waste management, resource conservation and recovery, and soil and ground water and surface water remediation. The aim of this research is to investigate and discuss coronavirus and byssinosis disease impact assessment. Sustainable development is the artistic idea that science and humanities must live and meet their needs without compromising the efficacy and efficiency of future generations to meet their own needs. Prediction and assessment of environmental impacts (effects) on the design and arts environment for industry 3.0 cotton roller ginning process is provided. Sustainable design and arts are discussed in this research work. EIA of conventional design and arts is also investigated. Sustainable design and arts environment for cotton ginning process is presented. The case study and check of strengthening of agricultural extension through sustainable entrepreneurship is discussed in this article.
 
 Keywords: Agriculture, arts, cotton, design, environment, entrepreneurship, ginning, sustainability.

Life cycle assessment for hydrothermal carbonization of urban organic solid waste in comparison with gasification process: A case study of Southern Chile

AbstractMost developing countries lack sufficient legal and management infrastructure to dispose of urban solid waste (USW). The continuous increase of USW generation requires evaluating different treatments for developing countries based on the life cycle assessment methodology to compare the environmental impact by reducing greenhouse gases and leachate. Hydrothermal carbonization (HTC) and gasification processes are presented as potential solutions for USW treatment due to their efficiency in producing energy for local requirements. This study aimed to compare both technologies for Temuco and Padre Las Casas cities in Southern Chile that show severe air pollution and USW management problems. The results indicated that gasification had a better environmental performance than HTC when the conversion of 1 ton of organic fraction USW was analyzed. However, since HTC achieved higher energy efficiency, it had a lower environmental impact than gasification, considering the production of 1 MWh. For a definitive choice of the technology to be used, it is necessary to compare other variables, including economic and social aspects, to provide a holistic perspective.

Influence of transboundary air pollution on air quality in southwestern China

Air pollution is a grand challenge of our time due to its multitude of adverse impacts on environment and society, with the scale of impacts more severe in developing countries, including China. Thus, China has initiated and implemented strict air pollution control measures over last several years to reduce impacts of air pollution. Monitoring data from Jan 2015 to Dec 2019 on six criteria air pollutants (SO2, NO2, CO, O3, PM2.5, and PM10) at eight sites in southwestern China were investigated to understand the situation and analyze the impacts of transboundary air pollutants in this region. In terms of seasonal variation, the maximum concentrations of air pollutants at these sites were observed in winter or spring season depending on individual site. For diurnal variation, surface ozone peaked in the afternoon while the other pollutants had a bimodal pattern with peaks in the morning and late afternoon. There was limited transport of domestic emissions of air pollutants in China to these sites. Local emissions enhanced the concentrations of air pollutants during some pollution events. Mostly, the transboundary transport of air pollution from South Asia and Southeast Asia was associated with high concentrations of most air pollutants observed in southwestern China. Since air pollutants can be transported to southwestern China over long distances from the source regions, it is necessary to conduct more research to properly attribute and quantify transboundary transport of air pollutants, which will provide more solid scientific guidance for air pollution management in southwestern China.

Development and Evaluation of SLINE 1.0, a Line Source Dispersion Model for Gaseous Pollutants by Incorporating Wind Shear Near the Ground under Stable and Unstable Atmospheric Conditions

Transportation sources are a major contributor to air pollution in urban areas, and the role of air quality modeling is vital in the formulation of air pollution control and management strategies. Many models have appeared in the literature to estimate near-field ground level concentrations from mobile sources moving on a highway. However, current models do not account explicitly for the effect of wind shear (magnitude) near the ground while computing the ground level concentrations near highways from mobile sources. This study presents an analytical model (SLINE 1.0) based on the solution of the convective–diffusion equation by incorporating the wind shear near the ground for gaseous pollutants. The dispersion coefficients for stable and unstable atmospheric conditions are based on the near-field parameterization. Initial vertical dispersion coefficient due to the wake effect of mobile sources is incorporated based on a literature review. The model inputs include emission factor, wind speed, wind direction, turbulence parameters, and terrain features. The model is evaluated based on the Idaho Falls field study (2008). The performance of the model is evaluated using several statistical parameters. Results indicate that the model performs well against this dataset in predicting concentrations under both the stable and unstable atmospheric conditions. The sensitivity of the model to compute ground-level concentrations for different inputs is presented for three different downwind distances. In general, the model shows Type III sensitivity (i.e., the errors in the input will show a corresponding change in the computed ground level concentrations) for most of the input variables using the ASTM (American Society for Testing and Materials) method. However, some recalibration of the model constants is needed using several field datasets to make sure that the model is acceptable for computing ground-level concentrations in engineering applications.