Spatio-Temporal Characteristics of Tropospheric Ozone and Its Precursors in Guangxi, South China

Spatialized temporal dynamics of daily ozone concentrations: Identification of the main spatial differences

Ground-level ozone (O₃) is one of the most worrisome air pollutants regarding environmental and health impacts. There is a need for a deeper understanding of its spatial and temporal dynamics. Models are needed to provide continuous temporal and spatial coverage in ozone concentration data with a fine resolution. However, the simultaneous influence of each determinant of ozone dynamics, their spatial and temporal variations, and their interaction make the resulting dynamics of O₃ concentrations difficult to understand. This study aimed to i) identify different classes of temporal dynamics of O₃ at daily and 9 km² resolution over a long-term period of 12 years, ii) identify the potential determinants of these dynamics and, iii) explore the spatial distribution of the potential classes of temporal dynamics on a spatial continuum and over about 1000 km². Thus, 126 time series of 12-year daily ozone concentrations were classified using dynamic time warping (DTW) and hierarchical clustering (study area centered on Besançon, eastern France). The different temporal dynamics obtained differed on elevation, ozone levels, proportions of urbanized and vegetated surfaces. We identified different daily ozone temporal dynamics, spatially structured, that overlapped areas called urban, suburban and rural. Urbanization, elevation and vegetation acted as determinants simultaneously. Individually, elevation and vegetated surface were positively correlated with O₃ concentrations (r = 0.84 and r = 0.41, respectively), while the proportion of urbanized area was negatively correlated with O₃ (r = -0.39). An increasing ozone concentration gradient was observed from urban to rural areas and was reinforced by the elevation gradient. Rural areas were both subject to higher ozone levels (p < 0.001), least monitoring and lower predictability. We identified main determinants of the temporal dynamics of ozone concentrations. The joint influence of determinants was also synthesized. This study proposed a systematic, and reproducible way to build exposure area mapping.

Time series analysis and spatial distribution map of aggregate risk index due to tropospheric NO2 and O3 based on satellite observation

A high increase in human activities has led to more emission of air pollutants in metropolises and industrial areas. Recently, remotely sensed data of tropospheric pollutants is used for environmental management and decision-making on large scale. The purpose of this study was a time series analysis of nitrogen dioxide Vertical Column Density (NO₂ VCD) and Ozone (O₃) using Ozone Monitoring Instrument (OMI) from 2005 to 2016 by Mann-Kendall test. Also, the aggregate risk index (ARI) was calculated to estimate the overall impact of exposure to tropospheric NO₂ and O₃ concentrations at the national scale in 2016. To estimate the surface NO₂ related drivers, The Radial Basis Function (RBF) neural network modeling was performed for different months of 2016. Results of Mann-Kendall test showed that tropospheric ozone concentration had an increasing trend in all parts of Iran and this increasing trend was significantly higher in the southern region of Iran and lower in the northern parts of Iran. NO₂ VCD in most parts of Iran had a significant increasing trend. The result of sensitivity analysis showed that NO₂ VCD (1.25), the distance to the industrial area, (1.20) and wind speed (1.07) were the most important variables for the estimation of surface NO₂ concentration. Spatial ARI with the highest risks is mainly located in the Northern half of Iran, especially in Tehran, Alborz, and Khorasan-e- Razavi provinces, where NO₂ and O₃ concentrations are very severe. In northern Iran and central cities, the ARI values are calculated from 1.5 to 2.08, indicating the highest human health risks in these regions. The human health risks based on OMI observation were obtained higher in comparison to AQM data because the satellite data coverage is larger than AQM station and monitors transmitted air pollution by the wind in addition to local pollution. Based on this research, using satellite observation for air quality monitoring is a suitable tool for environmental management on a national scale.

Assessing the intra-urban variability of nitrogen oxides and ozone across a highly heterogeneous urban area

High-resolution air quality maps are critical towards assessing and understanding exposures to elevated air pollution in dense urban areas. However, these surfaces are rarely available in low- and middle-income countries that suffer from some of the highest air pollution levels worldwide. In this study, we make use of land use regressions (LURs) to generate annual and seasonal, high-resolution nitrogen dioxide (NO₂), nitrogen oxides (NO_x), and ozone (O₃) exposure surfaces for the Greater Beirut Area (GBA) in Lebanon. NO₂, NO_x and O₃ concentrations were monitored using passive samplers that were deployed at 55 pre-defined monitoring locations. The average annual concentrations of NO₂, NO_x, and O₃ across the GBA were 36.0, 89.7, and 26.9 ppb, respectively. Overall, the performance of the generated models was appropriate, with low biases, high model robustness, and acceptable R² values that ranged between 0.66 and 0.73 for NO₂, 0.56 and 0.60 for NO_x, and 0.54 and 0.65 for O₃. Traffic-related emissions as well as the operation of a fossil-fuel power plant were found to be the main contributors to the measured NO₂ and NO_x levels in the GBA, whereas they acted as sinks for O₃ concentrations. No seasonally significant differences were found for the NO₂ and NO_x pollution surfaces; as their seasonal and annual models were largely similar (Pearson's r > 0.85 for both pollutants). On the other hand, seasonal O₃ pollution surfaces were significantly different. The model results showed that around 99% of the population of the GBA were exposed to NO₂ levels that exceeded the World Health Organization defined annual standard.

Characteristics of Ozone Pollution, Regional Distribution and Causes during 2014–2018 in Shandong Province, East China

The summer ozone pollution of Shandong province has become a severe problem in the period 2014–2018. Affected by the monsoon climate, the monthly average ozone concentrations in most areas were unimodal, with peaks in June, whereas in coastal areas the concentrations were bimodal, with the highest peak in May and the second highest peak in September. Using the empirical orthogonal function method, three main spatial distribution patterns were found. The most important pattern proved the influences of solar radiation, temperature, and industrial structure on ozone. Spatial clustering analysis of the ozone concentration showed Shandong divided into five units, including Peninsula Coastal area (PC), Lunan inland area (LN), Western Bohai area (WB), Luxi plain area (LX), and Luzhong mountain area (LZ). Influenced by air temperature and local circulation, coastal cities had lower daytime and higher nighttime ozone concentrations than inland. Correlation analysis suggested that ozone concentrations were significantly positively correlated with solar radiation. The VOCs from industries or other sources (e.g., traffic emission, petroleum processing, and chemical industries) had high positive correlations with ozone concentrations, whereas NOx emissions had significantly negatively correlation. This study provides a comprehensive understanding of ozone pollution and theoretical reference for regional management of ozone pollution in Shandong province.