The evaluation of carbon monoxide emissions using regression analysis and GIS techniques

Abstract

In the last few years, Baghdad city has witnessed substantial changes in the land use/land cover, a significant increase in the number of inhabitants, number of vehicles, and industrial activities. As a result, air pollutants from traffic, industries, cooking, and waste incineration are relatively increased. Moreover, Baghdad city is considered an attractive location for a large number of humans, where the potential for human exposure to relatively high air pollutants makes Baghdad city a good target of investigation in recent studies related to air pollution in urban areas. Carbon Monoxide (CO) is one of the air pollutants, which is considered colourless and odourless mainly emitted from the uncompleted process of fuel combustion. CO has negative impacts on human health. The evaluation of Carbon monoxide in urban areas usually requires the implementation of appropriate methods, which can enable an understanding of the relationship between CO sources and receptors to adopt proper strategies for pollution mitigation within urban areas. In this study, we proposed a model based on the integration between GIS data and regression analysis to predict hourly CO concentrations in an urban area, Baghdad, Iraq. Regression modelling was carried out depending on independent variables (Temperature, Humidity, Wind speed, Wind direction, Solar Radiation, Built-up area, Agricultural lands, Water, Barren land, Road length) and dependent variables included samples of hourly CO concentrations during 2019. The modelling results indicated that the correlation coefficient was achieved at 0.8845, while the Root mean squared error was 0.1208 ppm. On the other hand, the R-squared was 0.74 according to the testing data and 0.78 according to the training data. The presented regression model might be adopted as an evaluation tool for the air pollutants studies in Iraq. The presented model might be used as an efficient tool for the air pollutants evaluation in Iraq. This can support private agencies and the government in Iraq. The proposed regression model can be upgraded to be more generalized for other locations in Iraq by collecting more environmental and geographic information like air pollutants related to CO such as CO2, traffic data, roads condition, building height, and elevation that can improve the proposed model.

Comparison of Air Pollutants during the Two COVID-19 Lockdown Periods in Winter 2019 and Spring 2022 in Shanghai, China

“…However, under the same lockdown conditions in Shanghai, the concentration of CO in spring lockdown increased (Table 2). It is known that the CO concentration is primarily influenced by human activities [27], and this increase during the spring lockdown period can be attributed to the transmission of airflow from the surrounding areas that were not under lockdown. Consequently, it is expected that there should have been a reduction in the O 3 concentration during the spring lockdown period because of the increase in the CO concentration.…”