Effects of Air Pollutants on Summer Precipitation in Different Regions of Beijing

Many studies have shown that air pollutants have complex impacts on urban precipitation. Meteorological weather station and satellite Aerosol Optical Depth (AOD) product data from the last 20 years, combined with simulation results from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), this paper focuses on the effects of air pollutants on summer precipitation in different regions of Beijing. These results showed that air pollution intensity during the summer affected the precipitation contribution rate (PCR) of plains and mountainous regions in the Beijing area, especially in the plains. Over the past 20 years, plains PCR increased by ~10% when the AOD augmented by 0.15, whereas it decreased with lower pollution levels. In contrast, PCR in mountainous areas decreased with higher pollution levels and increased with lower pollution levels. Our analysis from model results indicated that aerosol increases reduce the effective particle size of cloud droplets and raindrops. Smaller cloud raindrops more readily transport to high air layers and participate in the generation of ice-phase substances in the clouds, increasing the total amount of cloud water in the air in a certain time, which ultimately enhanced precipitation intensity on the plains. The removal of pollutants caused by increased precipitation in the plains decreased rainfall levels in mountainous areas.

Air Pollution in Poland: A 2022 Narrative Review with Focus on Respiratory Diseases

According to the World Bank Group, 36 of the 50 most polluted cities in the European Union are in Poland. Thus, ambient air pollution and its detrimental health effects are a matter of immense importance in Poland. This narrative review aims to analyse current findings on air pollution and health in Poland, with a focus on respiratory diseases, including COVID-19, as well as the Poles’ awareness of air pollution. PubMed, Scopus and Google Scholar databases were searched. In total, results from 71 research papers were summarized qualitatively. In Poland, increased air pollution levels are linked to increased general and respiratory disease mortality rates, higher prevalence of respiratory diseases, including asthma, lung cancer and COVID-19 infections, reduced forced expiratory volume in one second (FEV1) and forced vital capacity (FVC). The proximity of high traffic areas exacerbates respiratory health problems. People living in more polluted regions (south of Poland) and in the winter season have a higher level of air pollution awareness. There is an urgent need to reduce air pollution levels and increase public awareness of this threat. A larger number of multi-city studies are needed in Poland to consistently track the burden of diseases attributable to air pollution.

PCA-Based Identification of Built Environment Factors Reducing PM2.5 Pollution in Neighborhoods of Five Chinese Megacities

Air pollution, especially PM2.5 pollution, still seriously endangers the health of urban residents in China. The built environment is an important factor affecting PM2.5; however, the key factors remain unclear. Based on 37 neighborhoods located in five Chinese megacities, three relative indicators (the range, duration, and rate of change in PM2.5 concentration) at four pollution levels were calculated as dependent variables to exclude the background levels of PM2.5 in different cities. Nineteen built environment factors extracted from green space and gray space and three meteorological factors were used as independent variables. Principal component analysis was adopted to reveal the relationship between built environment factors, meteorological factors, and PM2.5. Accordingly, 24 models were built using 32 training neighborhood samples. The results showed that the adj_R2 of most models was between 0.6 and 0.8, and the highest adj_R2 was 0.813. Four principal factors were the most important factors that significantly affected the growth and reduction of PM2.5, reflecting the differences in green and gray spaces, building height and its differences, relative humidity, openness, and other characteristics of the neighborhood. Furthermore, the relative error was used to test the error of the predicted values of five verification neighborhood samples, finding that these models had a high fitting degree and can better predict the growth and reduction of PM2.5 based on these built environment factors.

The Effects of Traffic Air Pollution in and around Schools on Executive Function and Academic Performance in Children: A Rapid Review

This review summarises the extant literature investigating the relation between traffic-related air pollution levels in and around schools and executive functioning in primary-school-aged children. An electronic search was conducted using Web of Science, Scopus, and Education Literature Datasets databases (February 2020). Review articles were also searched, and forwards and backwards searches of identified studies were performed. Included papers were assessed for quality. We included 9 separate studies (published in 13 papers). Findings suggest that indoor and outdoor particulate matter with a diameter of 2.5 μm or less (PM2.5) negatively influences executive function and academic achievement and that indoor and outdoor nitrogen dioxide (NO2) adversely affects working memory. Evidence for the effects of particulate matter with a diameter of 10 μm or less (PM10) is limited but suggests potential wide-ranging negative effects on attention, reasoning, and academic test scores. Air pollution in and around schools influences executive function and appears to impede the developmental trajectory of working memory. Further research is required to establish the extent of these effects, reproducibility, consequences for future attainment, and place within the wider context of cognitive development.

Long-term behavior and stability of calibration models for NO and NO₂ low cost sensors

Low-cost sensors are considered as exhibiting great potential to complement classical air quality measurements in existing monitoring networks. However, the use of low-cost sensors poses some challenges. In this study, the behavior and performance of electrochemical sensors for NO and NO2 were determined over a longer operating period in a real-world deployment. After careful calibration of the sensors, based on co-location with reference instruments at a rural traffic site during six months and by using robust linear regression and random forest regression, the coefficient of determination of both types of sensors were high (R2 > 0.9) and the root mean square error (RMSE) of NO and NO2 sensors were about 6.8 ppb and 3.5 ppb, respectively, for 10-minute mean concentrations. The RMSE of the NO2 sensors, however, more than doubled, when the sensors were deployed without re-calibration for a one-year period at other site types (including urban background locations), where the range and the variability of air pollutant concentrations differed from the calibration site. This indicates a significant effect of the re-location of the sensors on the quality of their data. During deployment, we found that the NO2 sensors are capable of distinguishing general pollution levels, but they proved unsuitable for accurate measurements, mainly due to significant biases. In order to investigate the long-term stability of the original calibration, the sensors were re-installed at the calibration site after deployment. Surprisingly, the coefficient of determination and the RMSE of the NO sensor remained almost unchanged after more than one year of operation. In contrast, the performance of the NO2 sensors clearly deteriorated as indicated by a higher RMSE (about 7.5 ppb, 10-minute mean concentrations) and a lower coefficient of determination (R2 = 0.59).

Seasonal and Spatial Variation in Dissolved Heavy Metals in Liaodong Bay, China

Spatial–seasonal variations in dissolved heavy metals in surface seawater were analyzed based on surveys at 87 sampling sites and water samples from six rivers across Liaodong Bay. The concentrations of copper (Cu), lead (Pb), cadmium (Cd), and zinc (Zn) had ranges of 0.20–40.00 (5.45 ± 5.67), 0.51–33.64 (4.68 ± 3.93), 0.03–13.47 (2.22 ± 2.01), and 0.50–80.09 μg/L (14.22 ± 16.32), respectively, throughout the four seasons of 2020. The trace metal concentration showed a spatial gradient of high to low from river to estuary and from inshore to offshore areas. A combination of pollution levels and marine sensitivity was employed to assess the pollution degree of the heavy metals. As a whole, the single pollution factors of trace metals in Liaodong Bay were ranged in the order Pb > Zn > Cu > Cd. The total pollution degree was relatively high in autumn and summer due to increased riverine inputs after the rainy season, while relatively low in spring and winter. These findings provide baseline data for future targeting policies to protect marine environments in Liaodong Bay.

Air Pollution and Climate Change

Climate records kept worldwide clearly show that ongoing changes are happening in our eco-systems. Such climate changes include temperature, precipitation, or sea level, all of which are expected to keep changing well into the future, thereby affecting human health, the environment, and the economy. The natural causes by themselves are not able to describe these changes, so to understand these, scientists are using a combination of state-of-the-science measurements and models. Human activities are a major contributor due to the release of different air contaminants through various activities. Air pollution is one case-in-point, a human-made factor that contributes to climate change by affecting the amount of incoming sunlight that is either reflected or absorbed by the atmosphere. An overview of modeling techniques used to relate air quality and climate change is presented. The discussion includes the role of air pollution levels affecting the climate. Emerging topics such as black carbon (BC), fine particulate matters (PMs), role of cook stove, and risk assessment are also covered.

The Role of the Government in Environmental Sustainability During the COVID-19 Pandemic

The COVID-19 outbreak and its global spread through human-to-human contact have made it even more important to analyze the environmental effects. The higher the population, the higher the energy usage, the higher amount of carbon emissions, and the faster the environmental degradation. Having a high-quality environment is important for people to protect themselves from infection. During the lockdowns, city residents could benefit from the environment. Shutdowns contributed not only to break the chain of infections but also to the development of the environment and ecosystems. Due to the great cuts in transportation and industrial sectors, air and water pollution levels have come down, and nature has started to reassert itself. In this process, governments have a great role to fight the pandemic and protect the environment. In this chapter, environmental sustainability and the role of governments during the pandemic will be analyzed. Also, the viable solutions for environmental sustainability that can be provided by the governments will be put forth.

Subways and Urban Air Pollution

We investigate the effect of subway system openings on urban air pollution. On average, particulate concentrations are unchanged by subway openings. For cities with higher initial pollution levels, subway openings reduce particulates by 4 percent in the area surrounding a city center. The effect decays with distance to city center and persists over the longest time horizon that we can measure with our data, about four years. For highly polluted cities, we estimate that a new subway system provides an external mortality benefit of about $1 billion per year. For less polluted cities, the effect is indistinguishable from zero. Back of the envelope cost estimates suggest that reduced mortality due to lower air pollution offsets a substantial share of the construction costs of subways. (JEL I12, L92, O13, O18, Q51, Q53, R41)

Embedded Generative Air Pollution Model with Variational Autoencoder and Environmental Factor Effect in Ulaanbaatar City

Air pollution is one of the most pressing modern-day issues in cities around the world. However, most cities have adopted air quality measurement devices that only measure the past pollution levels without paying attention to the influencing factors. To obtain preliminary pollution information with regard to environmental factors, we developed a variational autoencoder and feedforward neural network-based embedded generative model to examine the relationship between air quality and the effects of environmental factors. In the model, actual SO2, NO2, PM2.5, PM10, and CO measurements from 2016 to 2020 were used, which were assembled from 15 differently located ground monitoring stations in Ulaanbaatar city. A wide range of weather and fuel measurements were used as the data for the influencing factors, and were collected over the same period as the air pollution data were recorded. The prediction results concerned all measurement stations, and the results were visualized as a spatial–temporal distribution of pollution and the performance of individual stations. A cross-validated R2 was used to estimate the entire pollution distribution through the regions as SO2: 0.81, PM2.5: 0.76, PM10: 0.89, and CO: 0.83. Pearson’s chi-squared tests were used for assessing each measurement station, and the contingency tables represent a high correlation between the actual and model results. The model can be applied to perform specific analysis of the interdependencies between pollution and environmental factors, and the performance of the model improves with long-range data.