Mapping Carbon Monoxide Pollution of Residential Areas in a Polish City

Impacts of Built-Environment on Carbon Dioxide Emissions from Traffic: A Systematic Literature Review

With the acceleration of global urbanization, the interaction between the urban built environment and transportation carbon emissions (TCE) has become an urgent problem and an area of intensive research. This paper presents a bibliometric and visual analysis of 1060 pieces of literature related to the built environment and TCE from 1998 to 2022. It explores the current research progress and future development trends in this field. The results show the following. (1) The number of papers published on the built environment and TCE during this period has shown a continuous increasing trend, and the periods of growth can be divided into three stages. (2) Research in this area has been carried out in many countries and regions around the world, involving different dimensions such as examinations at the city, provincial, and national levels. (3) Through an analysis involving keyword clustering, a keyword hotspot map, and a burst map, we have established that the research on TCE has exhibited step-by-step growth, and the carbon emissions from vehicles is the topic that has been considered over the longest period. (4) The impact of the built environment on TCE can be broadly divided into macro-functional and micromorphological factors.

Verification of the Perception of the Local Community concerning Air Quality Using ADMS-Roads Modeling

Road transport is one among the sources of air pollution in a city, which results in lowering the comfort of life and increases the occurrence of respiratory diseases. The level of pollutants emitted in the city is variable, and it depends on the type and nature of the source and the manner of land development. For this reason, the purpose of the article is an attempt at a spatial (inner) diversification of a city in terms of air quality, using a study of perception and semantic differentials (SD). The research, which covered the period from June to November 2021, was performed in Kielce-the Polish Smart City-among local experts, people well acquainted with the city and knowledgeable about air quality and the impact of pollution on human health. The results allowed the demarcation of areas with the best and the worst parameters in terms of air quality within the city. Verification of the survey was carried out using the ADMS-Roads (Atmospheric Dispersion Modeling System) software for modeling pollution levels and GIS software, using data on road traffic. The verification allowed checking whether the respondents participating in the research accurately evaluated the city space. The modeling proved that within the two selected areas, the pollution level is similar, and it does not exceed the permitted values. This might indicate that in society there is still low awareness of air quality, particularly in terms of knowing the sources of pollutants and their impact on human health, and perception of areas with the best and the worst air quality was the result of an analysis of the manner of land development and its morphology.

The Landscape of the Spa Parks Creation through Components Influencing Environmental Perception Using Multi-Criteria Analysis

The improvement of the landscape of spa parks should result from the planned directions of development and the most important tasks of the spatial policy of spa towns. Their proper management significantly impacts the region’s economic growth: the labour market, the size and type of new financial investments, and the natural environment. The basis of activity in the areas of spa parks is the balancing of elements of the natural, cultural and historical background. Parks are health protection zones, health ecosystems—separated from the spa zone to protect healing and environmental values. These areas should be characterized by rational landscaping. The spatial layout of park areas should consider the appropriate zones (healing, silence, recreation). The leading element that allows separating sub-areas in parks is the layout of alleys and greenery. The attractiveness of parks is also influenced by factors related to spa activities. The article aims to indicate the main criteria affecting the perception of the landscape of spa parks. Their meaning was determined by information entropy. A selection of criteria defining the landscape assessment and influencing the perception of spa greenery, which helped determine how to shape and develop the spa park, was presented. Data obtained from actual field measurements on factors influencing the reception and perception of spa parks were considered. The multi-criteria method of comparative analysis allows one to quickly and transparently determine the hierarchy of the most advantageous variables from the adopted criteria. The study confirmed that the smaller the sum of adverse elements affecting the shaping of the park spatial, the better residents’ and patients’ perception of the park.

Design of a Spark Big Data Framework for PM2.5 Air Pollution Forecasting

In recent years, with rapid economic development, air pollution has become extremely serious, causing many negative effects on health, environment and medical costs. PM_2.5 is one of the main components of air pollution. Therefore, it is necessary to know the PM_2.5 air quality in advance for health. Many studies on air quality are based on the government’s official air quality monitoring stations, which cannot be widely deployed due to high cost constraints. Furthermore, the update frequency of government monitoring stations is once an hour, and it is hard to capture short-term PM_2.5 concentration peaks with little warning. Nevertheless, dealing with short-term data with many stations, the volume of data is huge and is calculated, analyzed and predicted in a complex way. This alleviates the high computational requirements of the original predictor, thus making Spark suitable for the considered problem. This study proposes a PM_2.5 instant prediction architecture based on the Spark big data framework to handle the huge data from the LASS community. The Spark big data framework proposed in this study is divided into three modules. It collects real time PM_2.5 data and performs ensemble learning through three machine learning algorithms (Linear Regression, Random Forest, Gradient Boosting Decision Tree) to predict the PM_2.5 concentration value in the next 30 to 180 min with accompanying visualization graph. The experimental results show that our proposed Spark big data ensemble prediction model in next 30-min prediction has the best performance (R² up to 0.96), and the ensemble model has better performance than any single machine learning model. Taiwan has been suffering from a situation of relatively poor air pollution quality for a long time. Air pollutant monitoring data from LASS community can provide a wide broader monitoring, however the data is large and difficult to integrate or analyze. The proposed Spark big data framework system can provide short-term PM_2.5 forecasts and help the decision-maker to take proper action immediately.

Estimation of Ultrahigh Resolution PM2.5 Mass Concentrations Based on Mie Scattering Theory by Using Landsat8 OLI Images over Pearl River Delta

The aerosol optical depth (AOD), retrieved by satellites, has been widely used to estimate ground-level PM2.5 mass concentrations, due to its advantage of large-scale spatial continuity. However, it is difficult to obtain urban-scale pollution patterns from the coarse resolution retrieval results (e.g., 1 km, 3 km, or 10 km) at present, and little research has been conducted on PM2.5 mass concentration retrieval from high resolution remote sensing data. In this study, a physical model is proposed based on Mie scattering theory to evaluate the PM2.5 mass concentrations by using Landsat8 Operational Land Imager (OLI) images. First, the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) model (which can simulate the transmission process of solar radiation in the Earth-atmosphere system and calculate the radiance at the top of the atmosphere) is used to build a lookup table to retrieve the AOD of the coast and blue bands based on the improved deep blue (DB) method. Then, the Angstrom formula is used to obtain the AOD of the green and red bands. Second, the dry near-surface AOD of four bands (coast, blue, green, red) is obtained through vertical correction and humidity correction. Third, aerosol particles are divided into four types based on the standard radiation atmosphere (SRA) model, and the optical properties of different aerosol types are analyzed to derive the volume distribution of aerosol particles. Finally, the relationship between the dry near-surface AOD of each band and the volume distribution of four aerosol particles is correlated, based on Mie scattering theory, and a physical model is established between the AOD and PM2.5 mass concentrations. Then, the distribution of PM2.5 mass concentrations is obtained. The retrieval results show that the distribution of AOD and PM2.5 at the urban scale in detail. The AOD results show that a reasonable relationship with a correlation coefficient (R2) of 0.66 and root mean square error (RMSE) of 0.1037 between Landsat8 OLI AOD and MODO4 DB AOD at 550 nm. The PM2.5 retrieval results are compared with the PM2.5 values measured by ground monitoring stations. The RMSEs for a certain day in different years, including 2017, 2018, 2019, and 2020, are 11.9470 μg/m³, 11.9787 μg/m³, 7.4217 μg/m³, and 5.4723 μg/m³, respectively. The total RMSE is 10.0224 μg/m³. The ultrahigh resolution PM2.5 results can provide pollution details at the urban scale and support better decisions on urban atmospheric environmental governance.