Abstract:The main aims of urban air pollution monitoring are to optimize the interaction between humanity and nature, to combine and integrate environmental databases, and to develop sustainable approaches to the production and the organization of the urban environment. One of the main applications of urban air pollution monitoring is for exposure assessment and public health studies. Artificial intelligence (AI) and machine learning (ML) approaches can be used to build air pollution models to predict pollutant concent… Show more
“…The most successful global approach is the development of the smart city. However, such an approach can only increase environmental injustice as not all regions have access to AI/ML technologies (Krupnova et al, 2022). The last direction comprises a complete air quality decision support system that includes three subsystems, namely air quality forecast, air quality evaluation, and environmental impact estimation.…”
Many studies have demonstrated that air quality is an important factor affecting national well-being. Is there a relationship between air pollution and people’s happiness? To explore this, more than 40,000 haze-related tweets on the Chinese largest microblog platform SinaWeibo were collected. Using daily data for 6 Chinese cities from January 1 to February 12, 2023, we applied natural language processing (NLP) to analyze theWeibo tweets and construct a daily city-level expressed happiness metric. A fixed effects model was applied to reveal the relationship between air pollution and happiness. We found that
a one standard deviation rise in the Air Quality Index corresponded to a 0.042 standard deviation fall in the happiness index on average. People in large and rich cities are more sensitive to air pollution, and people suffer more from air pollution on weekends and holidays than on workdays. This project may provide new insights into air pollution and public engagement, and help governments and related institutions to better understand the public’s needs regarding air quality.
“…The most successful global approach is the development of the smart city. However, such an approach can only increase environmental injustice as not all regions have access to AI/ML technologies (Krupnova et al, 2022). The last direction comprises a complete air quality decision support system that includes three subsystems, namely air quality forecast, air quality evaluation, and environmental impact estimation.…”
Many studies have demonstrated that air quality is an important factor affecting national well-being. Is there a relationship between air pollution and people’s happiness? To explore this, more than 40,000 haze-related tweets on the Chinese largest microblog platform SinaWeibo were collected. Using daily data for 6 Chinese cities from January 1 to February 12, 2023, we applied natural language processing (NLP) to analyze theWeibo tweets and construct a daily city-level expressed happiness metric. A fixed effects model was applied to reveal the relationship between air pollution and happiness. We found that
a one standard deviation rise in the Air Quality Index corresponded to a 0.042 standard deviation fall in the happiness index on average. People in large and rich cities are more sensitive to air pollution, and people suffer more from air pollution on weekends and holidays than on workdays. This project may provide new insights into air pollution and public engagement, and help governments and related institutions to better understand the public’s needs regarding air quality.
“…Furthermore, the dense population in urban areas makes it important yet difficult to accurately assess exposure. A vital aspect to focus on is the characterization of within-city air pollutant concentration gradients, which play a significant role in exposure assessment [14], urban planning [15,16], air pollution monitoring [13], and environmental equity [17].…”
Black carbon (BC) is a significant source of air pollution since it impacts public health and climate change. Understanding its distribution in the complex urban environment is challenging. We integrated a land use model with four machine learning models to estimate traffic-related BC concentrations in Oakland, CA. Random Forest was the best-performing model, with regression coefficient (R2) values of 0.701 on the train set and 0.695 on the validation set with a root mean square error (RMSE) of 0.210 mg/m3. Vehicle speed and local road systems were the most sensitive variables in estimating BC concentrations. However, this approach was inefficient at identifying hyperlocal hotspots, especially in a complex urban environment where highways and truck routes are significant emission sources. Using the land use method to estimate BC concentrations may lead to underestimating some localized hotspots. This work can improve air quality exposure assessment for vulnerable populations and help emphasize potential environmental justice issues.
“…Monitoring of the urban environment, including urban air pollution monitoring, helps to assess the reduction of risks to public health and to form a sustainable urban environment [6]. In recent years, remote sensing technology has become widely used in digital near real-time monitoring of urban areas pollution.…”
The dynamic development of modern cities requires new solutions to urban planning and management by local regional authorities. The paper focuses on ecological indicators based on Earth Remote Sensing Data (ERSD) of the snow cover with the purpose to evaluate the city and to plan ecological environment protection strategy. The paper deals with the method of using space images to assess the snow cover pollution of Chelyabinsk, a large Russian industrial city. The assessment of the snow cover of Chelyabinsk was carried out by comparing the heavy metals concentrations with the Landsat 8 data. The spectral indices were calculated for fourteen sites evenly distributed over the urban area of four types: courtyards, car parks, industrial zones and roads. We found a statistically significant difference between the Swirl/Green index and the site type and a correlation with the concentrations of dissolved and suspended forms of heavy metals in snow cover. Snow cover indices can be used as ecological indicators of urban environment.
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