Spatiotemporal Variations in Summertime Ground-Level Ozone around Gasoline Stations in Shenzhen between 2014 and 2020
Abstract
:1. Introduction
2. Study Area and Data Source
2.1. Study Area
2.2. In Situ Ground-Level Ozone Data
2.3. Multisource Geospatial Datasets
3. Methods
3.1. GLM Analysis
3.2. BME Model
3.3. Model Evaluation
4. Results
4.1. Model Fitting and Evaluation
4.2. Distribution of GOCs in Shenzhen
4.3. Spatiotemporal Characteristics of GOCs at Gasoline Stations
4.4. Summertime Variations in GOCs around Gasoline Stations Based on Different Radii
5. Discussion
- (1)
- Attention should be given to studies on the effects of ground-level ozone pollution on human health in local hot spots, such as gasoline stations. Neighboring residents should reasonably arrange their travel time and activity sphere to reduce the adverse effects caused by ground-level ozone exposure.
- (2)
- Research on the measurement of VOCs and their effects on ground-level ozone formation in the atmosphere should be strengthened. Future epidemiological studies should focus more on the relationship between ground-level ozone and mortality, such as chronic cardiovascular and respiratory diseases.
- (3)
- Strict measures are required to control the emissions of ozone precursors (NOx and VOCs), which are mainly from motor vehicle exhaust. In the short term, replacing old cars with newer vehicles or eliminating old cars with subsidies are useful policy strategies. In the long term, developing public transportation to help reduce the use of private cars in major and populous cities is also needed.
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Number | Abbreviation | Description | Unit | Spatial Resolution | Temporal Resolution |
---|---|---|---|---|---|
1 | RH | relative humidity after moisture | 1 | 0.667° × 0.5° | Day |
2 | BCTP | bias corrected total precipitation | kgm−2 s−1 | 0.667° × 0.5° | Day |
3 | BCCMASS | black carbon column mass density | kgm−2 | 0.667° × 0.5° | Day |
4 | OCSMASS | organic carbon surface mass concentration | kgm−2 | 0.667° × 0.5° | Day |
5 | DUSMASS25 | dust surface mass concentration-PM2.5 | kgm−3 | 0.667° × 0.5° | Day |
6 | SO2SMASS | SO2 surface mass concentration | kgm−3 | 0.667° × 0.5° | Day |
7 | SO4SMASS | SO4 surface mass concentration | kgm−3 | 0.667° × 0.5° | Day |
8 | SSSMASS25 | sea salt column mass density-PM2.5 | kgm−3 | 0.667° × 0.5° | Day |
9 | PGENTOT | total column production of precipitation | kgm−2 s−1 | 0.667° × 0.5° | Day |
10 | QV2M | 2-m specific humidity | kg kg−1 | 0.667° × 0.5° | Day |
11 | LST | land surface temperature | ° C | 1 km × 1 km | Day |
12 | RD | road density | km/km2 | Polyline | Year |
13 | LON | longitude | ° | NA | NA |
14 | LAT | latitude | ° | NA | NA |
15 | DNS | day number sequence | NA | NA | NA |
Number | Variable | Coefficient | Confidential Interval | Correlation Coefficient |
---|---|---|---|---|
1 | Constant | 3.49 | [3.45, 3.52] | |
2 | RH | 0.12 | [0.08, 0.16] | −0.01 |
3 | BCTP | −1.68 | [−1.90, −1.47] | −0.05 |
4 | BCCMASS | 4.42 | [4.18, 4.65] | 0.02 |
5 | OCSMASS | −4.39 | [−4.64, −4.13] | 0.01 |
6 | DUSMASS25 | 0.49 | [0.43, 0.55] | −0.02 |
7 | SO2SMASS | −0.45 | [−0.51, −0.39] | −0.16 |
8 | SO4SMASS | −0.18 | [−0.22, −0.13] | −0.12 |
9 | SSSMASS25 | −0.35 | [−0.40, −0.30] | −0.13 |
10 | LST | 1.30 | [1.26, 1.35] | 0.26 |
11 | LON | 0.10 | [0.08, 0.13] | 0.02 |
12 | LAT | 0.05 | [0.03, 0.07] | 0.02 |
13 | DNS | 0.11 | [0.10, 0.13] | 0.06 |
Year | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 |
---|---|---|---|---|---|---|---|
Difference | 0.38 * | 0.48 * | 1.76 * | −0.30 | 1.15 * | −0.26 | −0.63 * |
2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | ||
---|---|---|---|---|---|---|---|---|
Radius | 1 km | 30.24 | 32.35 | 36.88 | 32.12 | 33.98 | 35.68 | 29.31 |
2 km | 30.23 | 32.34 | 36.80 | 32.10 | 33.93 | 35.65 | 29.33 | |
3 km | 30.18 | 32.31 | 36.72 | 32.09 | 33.88 | 35.63 | 29.35 | |
Citywide level | 29.81 | 31.84 | 35.18 | 32.42 | 32.89 | 35.83 | 28.69 |
Buffer Zone Pairs | Year | ||||||
---|---|---|---|---|---|---|---|
2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
I versus II | 0.01 * | 0.01 * | 0.08 * | 0.02 * | 0.05 * | 0.03 * | −0.02 |
I versus III | 0.06 * | 0.04 * | 0.16 * | 0.03 | 0.10 * | 0.05 * | −0.04 |
II versus III | 0.05 * | 0.03 * | 0.08 * | 0.01 | 0.05 * | 0.02 | −0.02 |
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Mei, Y.; Xiang, X.; Xiang, D. Spatiotemporal Variations in Summertime Ground-Level Ozone around Gasoline Stations in Shenzhen between 2014 and 2020. Sustainability 2022, 14, 7289. https://doi.org/10.3390/su14127289
Mei Y, Xiang X, Xiang D. Spatiotemporal Variations in Summertime Ground-Level Ozone around Gasoline Stations in Shenzhen between 2014 and 2020. Sustainability. 2022; 14(12):7289. https://doi.org/10.3390/su14127289
Chicago/Turabian StyleMei, Yingying, Xueqi Xiang, and Deping Xiang. 2022. "Spatiotemporal Variations in Summertime Ground-Level Ozone around Gasoline Stations in Shenzhen between 2014 and 2020" Sustainability 14, no. 12: 7289. https://doi.org/10.3390/su14127289