Analysis and Modeling of Air Pollution in Extreme Meteorological Conditions: A Case Study of Jeddah, the Kingdom of Saudi Arabia

Air pollution has serious environmental and human health-related consequences; however, little work seems to be undertaken to address the harms in Middle Eastern countries, including Saudi Arabia. We installed a continuous air quality monitoring station in Jeddah, Saudi Arabia and monitored several air pollutants and meteorological parameters over a 2-year period (2018–2019). Here, we developed two supervised machine learning models, known as quantile regression models, to analyze the whole distribution of the modeled pollutants, not only the mean values. Two pollutants, namely NO₂ and O₃, were modeled by dividing their concentrations into several quantiles (0.05, 0.25, 0.50, 0.75, and 0.95) and the effect of several pollutants and meteorological variables was analyzed on each quantile. The effect of the explanatory variables changed at different segments of the distribution of NO₂ and O₃ concentrations. For instance, for the modeling of O₃, the coefficients of wind speed at quantiles 0.05, 0.25, 0.5, 0.75, and 0.95 were 1.40, 2.15, 2.34, 2.31, and 1.56, respectively. Correlation coefficients of 0.91 and 0.92 and RMSE values of 14.41 and 8.96, which are calculated for the cross-validated models of NO₂ and O₃, showed an acceptable model performance. Quantile analysis aids in better understanding the behavior of air pollution and how it interacts with the influencing factors.

Application of Density Plots and Time Series Modelling to the Analysis of Nitrogen Dioxides Measured by Low-Cost and Reference Sensors in Urban Areas

Temporal variability of NO2 concentrations measured by 28 Envirowatch E-MOTEs, 13 AQMesh pods, and eight reference sensors (five run by Sheffield City Council and three run by the Department for Environment, Food and Rural Affairs (DEFRA)) was analysed at different time scales (e.g., annual, weekly and diurnal cycles). Density plots and time variation plots were used to compare the distributions and temporal variability of NO2 concentrations. Long-term trends, both adjusted and non-adjusted, showed significant reductions in NO2 concentrations. At the Tinsley site, the non-adjusted trend was −0.94 (−1.12, −0.78) µgm−3/year, whereas the adjusted trend was −0.95 (−1.04, −0.86) µgm−3/year. At Devonshire Green, the non-adjusted trend was −1.21 (−1.91, −0.41) µgm−3/year and the adjusted trend was −1.26 (−1.57, −0.83) µgm−3/year. Furthermore, NO2 concentrations were analysed employing univariate linear and nonlinear time series models and their performance was compared with a more advanced time series model using two exogenous variables (NO and O3). For this purpose, time series data of NO, O3 and NO2 were obtained from a reference site in Sheffield, which were more accurate than the measurements from low-cost sensors and, therefore, more suitable for training and testing the model. In this article, the three main steps used for model development are discussed: (i) model specification for choosing appropriate values for p, d and q, (ii) model fitting (parameters estimation), and (iii) model diagnostic (testing the goodness of fit). The linear auto-regressive integrated moving average (ARIMA) performed better than the nonlinear counterpart; however, its performance in predicting NO2 concentration was inferior to ARIMA with exogenous variables (ARIMAX). Using cross-validation ARIMAX demonstrated strong association with the measured concentrations, with a correlation coefficient of 0.84 and RMSE of 9.90. ARIMAX can be used as an early warning tool for predicting potential pollution episodes in order to be proactive in adopting precautionary measures.

Analysis of Air Pollution in Urban Areas with Airviro Dispersion Model—A Case Study in the City of Sheffield, United Kingdom

Two air pollutants, oxides of nitrogen (NOx) and particulate matter (PM10), are monitored and modelled employing Airviro air quality dispersion modelling system in Sheffield, United Kingdom. The aim is to determine the most significant emission sources and their spatial variability. NOx emissions (ton/year) from road traffic, point and area sources for the year 2017 were 5370, 6774, and 2425, whereas those of PM10 (ton/year) were 345, 1449, and 281, respectively, which are part of the emission database. The results showed three hotspots of NOx, namely the Sheffield City Centre, Darnall and Tinsley Roundabout (M1 J34S). High PM10 concentrations were shown mainly between Sheffield Forgemasters International (a heavy engineering steel company) and Meadowhall Shopping Centre. Several emission scenarios were tested, which showed that NOx concentrations were mainly controlled by road traffic, whereas PM10 concentrations were controlled by point sources. Spatiotemporal variability and public exposure to air pollution were analysed. NOx concentration was greater than 52 µg/m3 in about 8 km2 area, where more than 66 thousand people lived. Models validated by observations can be used to fill in spatiotemporal gaps in measured data. The approach used presents spatiotemporal situation awareness maps that could be used for decision making and improving the urban infrastructure.

Numerical Modeling of the Natural and Manmade Factors Influencing Past and Current Changes in Polar, Mid-Latitude and Tropical Ozone

A chemistry–climate model of the lower and middle atmosphere is used to compare the role of natural and anthropogenic factors in the observed variability of stratospheric ozone. Numerical experiments have been carried out on several scenarios of separate and combined effects of solar activity, stratospheric aerosol, sea surface temperature, greenhouse gases, and ozone-depleting substances emissions on ozone for the period from 1979 to 2020. Simulations for the past and present periods are compared to the results of ground-based and satellite observations. Estimates of observed trends in column total ozone for the entire period 1980–2018 and separately for the late twentieth and early twenty-first century are presented.

Changes in biologically-active ultraviolet radiation reaching the Earth's surface

The Montreal Protocol is working. Concentrations of major ozone-depleting substances in the atmosphere are now decreasing, and the decline in total column amounts seen in the 1980s and 1990s at mid-latitudes has not continued. In polar regions, there is much greater natural variability. Each spring, large ozone holes continue to occur in Antarctica and less severe regions of depleted ozone continue to occur in the Arctic. There is evidence that some of these changes are driven by changes in atmospheric circulation rather than being solely attributable to reductions in ozone-depleting substances, which may indicate a linkage to climate change. Global ozone is still lower than in the 1970s and a return to that state is not expected for several decades. As changes in ozone impinge directly on UV radiation, elevated UV radiation due to reduced ozone is expected to continue over that period. Long-term changes in UV-B due to ozone depletion are difficult to verify through direct measurement, but there is strong evidence that UV-B irradiance increased over the period of ozone depletion. At unpolluted sites in the southern hemisphere, there is some evidence that UV-B irradiance has diminished since the late 1990s. The availability and temporal extent of UV data have improved, and we are now able to evaluate the changes in recent times compared with those estimated since the late 1920s, when ozone measurements first became available. The increases in UV-B irradiance over the latter part of the 20th century have been larger than the natural variability. There is increased evidence that aerosols have a larger effect on surface UV-B radiation than previously thought. At some sites in the Northern Hemisphere, UV-B irradiance may continue to increase because of continuing reductions in aerosol extinctions since the 1990s. Interactions between ozone depletion and climate change are complex and can be mediated through changes in chemistry, radiation, and atmospheric circulation patterns. The changes can be in both directions: ozone changes can affect climate, and climate change can affect ozone. The observational evidence suggests that stratospheric ozone (and therefore UV-B) has responded relatively quickly to changes in ozone-depleting substances, implying that climate interactions have not delayed this process. Model calculations predict that at mid-latitudes a return of ozone to pre-1980 levels is expected by the mid 21st century. However, it may take a decade or two longer in polar regions. Climate change can also affect UV radiation through changes in cloudiness and albedo, without involving ozone and since temperature changes over the 21st century are likely to be about 5 times greater than in the past century. This is likely to have significant effects on future cloud, aerosol and surface reflectivity. Consequently, unless strong mitigation measures are undertaken with respect to climate change, profound effects on the biosphere and on the solar UV radiation received at the Earth's surface can be anticipated. The future remains uncertain. Ozone is expected to increase slowly over the decades ahead, but it is not known whether ozone will return to higher levels, or lower levels, than those present prior to the onset of ozone depletion in the 1970s. There is even greater uncertainty about future UV radiation, since it will be additionally influenced by changes in aerosols and clouds.