Search

The USDA Forest Service has developed a unique software and tree modelling suite called i-Tree. Several of its instruments are capable of exploring the benefits of trees and forests for pollution mitigation, reduction of storm water runoff, carbon sequestration and storage. However, the system remains underutilized for investigating the effects of trees on air quality. In this study, counties in California (CA), United States, were selected using the i-Tree Landscape tool. Next, several characteristics including land cover details, forest details, population, air quality, carbon sequestration capacity, and air pollution removal capacity, were investigated. When considering the air quality situation in these counties, O3 and PM2.5 are the primary pollutants. The planting prioritization map of California was created based on population density, tree cover, plantable space, average PM2.5 and O3 concentration values, and the counties with the highest planting priority were selected using i-Tree Planting tool. Using this instrument, a case study on the modelling of the removal performances of these air pollutants by multiple new species (Turkey oak, Siberian elm, European hackberry, European white elm, common ash, European silver birch, velvet ash, black alder, bigleaf linden) in priority areas was conducted. The most effective modelled tree species in the area was found to be Turkey oak for its effects in improving air quality in general and O3 in particular. When compared to the effects of modelled trees, the effects of the existing public trees in California were determined to have a comparatively minor impact. [ABSTRACT FROM AUTHOR]

Geographical information systems are frequently used in analyses of air quality based on location and time. They are also used in the creation of pollution distribution maps to determine the parameters related to air pollutants. In this study, a spatial analysis of SO2, PM10, CO, NO2 and O3 pollutants, which cause air pollution within the borders of the municipal urban areas of Konya province, was carried out for the years 2019–2020. In this context, air pollution maps were produced using the IDW interpolation method with data obtained from the National Air Quality Monitoring Network stations, which belong to the Ministry of Environment and Urbanization, in the Konya region. The results obtained were examined with maps and graphics based on the limit values found in the Air Quality Assessment and Management Regulation published by the Ministry of Environment and Urbanization. In this context, the periods of lockdown experienced during the COVID-19 pandemic were also evaluated in terms of air pollution. From the evaluation made on the values taken from the air quality stations, it can be observed that the air pollution did not violate the national limit value much in 2019 and 2020. [ABSTRACT FROM AUTHOR]

Air pollution is one of the most important problems that negatively impacts human health and disrupts the ecological balance by changing the atmosphere because of the pollutants formed as a result of natural events and human activities. This problem is growing because of the increase in population, the development of industrialization and urbanization. Pollutants that cause air pollution reaching the atmosphere directly without changing their form are sulfur dioxide (SO2), hydrogen sulfide (H2S), nitrogen monoxide (NO), nitrogen dioxide (NO2), and carbon monoxide (CO), carbon dioxide (CO2) and particulate matter. Secondary pollutants are formed by reacting with other substances in the atmosphere after leaving the source are sulfur trioxide (SO3), sulfuric acid (H2SO4), ozone (O3), aldehydes, peroxyacetyl nitrate (PAN), and heavy metals. Besides, air pollution causes acid rain, increases acidity in lakes, destroys forests, damages agricultural and animal products, and significantly disrupts the ecological balance, especially in industrial countries Therefore, this issue should be evaluated in many ways such as modeling to predict future episode, monitoring to assess present air pollution levels efficiently and taking preventive precautions with respect to these evaluations. Artificial neural networks are one of the mostly used artificial intelligence prediction techniques for prediction of air pollutant future concentrations. It uses multilayer perceptron technique which consists of at least three layers of nodes: an input layer, a hidden layer, and an output layer for estimating recent atmospheric events and air quality. This study aims to examine the studies on the use of artificial neural network models to predict air pollution concentrations accurately and swiftly. It has been proven that the application of this method for air pollution prediction allows the improving of prediction accuracy. [ABSTRACT FROM AUTHOR]

Air pollution has negative effects on human health, visibility, materials, plants, and animal health. Particulate matters are one of the most important air pollutants that may create a risk for human health. Especially particulate matters, which are composed of heavy metals and cancer-causing chemicals such as PAH, dioxin, furan, can cause serious reactions in the respiratory tract. Heavy metals are so important because of their capability of accumulation in human tissues. Almost 0.01–3% of heavy metal content may be found in particulate matter. Coarse particulate matters (PM10) which have smaller diameters than 10 microns may enter from the respiratory system and reach the lungs. In this study, PM10 concentrations and heavy metal content (Lead, Nickel, Arsenic, Cadmium) of the samples were measured and evaluated concerning present regulations and limit values for different points in Konya Province, Turkey. The samples were taken at different seasons such as winter, summer, and spring for 16 days. According to the results of this study, in the winter season, PM10 concentration of the measurement point (Directorate Building) located at the settlement area was found the highest. In the summer season, PM10 concentration of the measurement point (Sille Junction) located at crossroads was found the highest. In spring season, maximum PM10 concentration was detected on the measurement point (KOS base station) located in the industrial area. Moreover, daily average nickel (Ni) concentration measured at KOS base station was found the highest and some other station located close to the industrial area and settlement areas were detected higher than average annual limit values in the winter period. Daily average lead (Pb) value was found at least 67% and maximum 98% higher at Sille and Beşyol Junctions, but below the annual average limits. Daily average cadmium (Cd) value was mostly calculated in the Directorate Building winter measurement, but it did not exceed the limit value during the measurement periods. Daily average arsenic (As) values at Directorate Building and Karkent measurement points in the winter period were found higher than the annual average limit values. Only daily average nickel concentrations were detected higher than the annual average limits for the summer and spring period at the KOS base station measurement point. [ABSTRACT FROM AUTHOR]