Spatial-Temporal Distribution and Variation of NO2 and Its Sources and Chemical Sinks in Shanxi Province, China
Abstract
:1. Introduction
2. Methods
2.1. Regional Overview
2.2. Data Sources
2.3. Statistical Analysis
2.3.1. Local Spatial Autocorrelation Analysis
2.3.2. Back Trajectory Analysis
2.3.3. Concentration Weighted Trajectory (CWT)
3. Results and Discussion
3.1. NO2 Pollution Level
3.2. NO2 Monthly Variations
3.3. NO2 Diurnal Variations
3.4. The Spatial Distribution and Local Spatial Autocorrelation of NO2
3.4.1. Spatial Distribution
3.4.2. Spatial Autocorrelation
3.5. Correlation between NO2 and Other Contaminants
3.5.1. The Roles of NO2 in O3 and PM2.5 Pollution
3.5.2. Air Pollution Type and Possible Sources
3.6. Regional Source
4. Conclusions
- (1)
- The concentrations of NO2 in winter were significantly higher than that in other seasons due to the heating-related increase of combustion emissions in cold days, and thus the over-standard days were mainly concentrated in the heating season. The over-standard rate was the highest in TY (15.60%), followed by LF (9.86%), XZ (9.15%), JZ (8.45%) and YQ (7.75%).
- (2)
- Spacial distribution analysis showed that the areas with high NO2 pollution mainly concentrated in the central part of Shanxi Province from southwest to northeast (LL/LF–TY/JZ–YQ/JZ). Spatial autocorrelation analysis indicated that TY was the hot spot of NO2 pollution in winter, while LL was the hot spot in summer. Besides coal combustion, traffic exhaust is a key source need to be controlled stringently to alleviate NO2 pollution in TY. An integrated planning and a detailed technical scheme based on quantitatively source apportionment is necessary for the government of LL to promote an overall decline of NO2 concentration over the year as a whole, because its annual average exceeded the national standard required by GB3095-2012, but no day was over-standard.
- (3)
- Under the cooperative influence of anthropogenic activities, terrain and meteorological conditions, the cities displayed four types of monthly variation characteristics. Solar radiation and air temperature strongly impacted the daily variation of NO2 concentrations and its sink reactions forming O3 and PM2.5, resulting in different spatial distribution of NO2 concentration from O3 and PM2.5, as well as seasonal difference of the correlation between NO2 and PM2.5.
- (4)
- The correlation analysis between NO2 and other air pollutants implies that coal burning, traffic exhaust, coke production and non-coal-burning industries with high-temperature combustion of natural gas should be important sources of NO2 in Shanxi. The dominant sources were different in different cities.
- (5)
- The central-north Shaanxi, central-south Shanxi, northern Henan, south of Shijiazhuang and areas around Erdos in Inner Mongolia were important source areas influencing the pollution situation of NO2 in Shanxi. Therefore, joint air pollution control with these areas is critical to solve the air pollution problems in Shanxi.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
Abbreviations
References
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City | Item | Annual | Spring | Summer | Autumn | Winter | Non-Heating d Period | Heating Period e |
---|---|---|---|---|---|---|---|---|
TY | Mean a | 48.93 | 46.50 | 36.87 | 52.93 | 59.45 | 41.63 | 58.61 |
Range b | 11.02–107.28 | 11.02–77.37 | 20.63–69.21 | 21.44–100.04 | 11.18–107.28 | 11.02–74.33 | 11.18–107.28 | |
Over-standard rate c (N) | 6.71 (22) | 0 (0) | 0 (0) | 7.41 (6) | 20.00 (16) | 0 (0) | 15.60 (22) | |
DT | Mean a | 28.84 | 27.61 | 21.60 | 32.04 | 33.92 | 24.68 | 34.54 |
Range b | 6.81–64.23 | 6.81–64.23 | 7.98–36.00 | 9.35–53.56 | 7.32–64.00 | 6.81–52.59 | 7.34–64.23 | |
Over-standard rate c (N) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | |
CZ | Mean a | 31.24 | 25.81 | 17.88 | 35.26 | 46.06 | 23.00 | 42.22 |
Range b | 5.42–82.82 | 8.32–42.92 | 5.42–42.04 | 9.09–82.82 | 20.84–78.19 | 5.42–48.18 | 12.89–82.82 | |
Over-standard rate c (N) | 0.30 (1) | 0 (0) | 0 (0) | 1.2 (1) | 0 (0) | 0 (0) | 0.70 (1) | |
LF | Mean a | 39.82 | 38.24 | 22.99 | 43.59 | 54.11 | 31.00 | 51.57 |
Range b | 6.71–97.01 | 6.71–63.16 | 7.91–45.89 | 15.24–87.45 | 8.37–97.01 | 6.71–63.53 | 8.37–97.01 | |
Over-standard rate c (N) | 4.23 (14) | 0 (0) | 0 (0) | 1.20 (1) | 16.05 (13) | 0 (0) | 9.86 (14) | |
YQ | Mean a | 44.03 | 44.07 | 30.26 | 46.81 | 54.11 | 36.66 | 53.95 |
Range b | 14.94–89.22 | 14.94–72.71 | 15.53–56.37 | 21.62–81.86 | 16.81–89.22 | 14.94–68.68 | 16.81–89.22 | |
Over-standard rate c (N) | 3.30 (11) | 0 (0) | 0 (0) | 1.15 (1) | 12.35 (10) | 0 (0) | 7.75 (11) | |
JC | Mean a | 37.17 | 37.96 | 27.15 | 39.94 | 43.10 | 33.28 | 42.45 |
Range b | 9.49–80.81 | 11.25–59.03 | 9.49–55.60 | 13.94–75.62 | 12.37–80.81 | 9.49–64.51 | 12.37–80.81 | |
Over-standard rate c (N) | 0.30 (1) | 0 (0) | 0 (0) | 0 (0) | 1.23 (1) | 0 (0) | 0.70 (1) | |
SZ | Mean a | 32.71 | 31.05 | 22.93 | 35.44 | 41.18 | 27.09 | 40.50 |
Range b | 6.74–73.24 | 6.74–60.28 | 10.22–46.70 | 10.35–64.23 | 7.97–73.24 | 6.74–60.28 | 7.97–73.24 | |
Over-standard rate c (N) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | |
JZ | Mean a | 44.29 | 42.18 | 28.50 | 50.20 | 55.80 | 36.19 | 55.46 |
Range b | 14.16–93.58 | 15.99–77.03 | 14.16–54.01 | 19.92–93.58 | 18.44–90.02 | 14.16–65.74 | 18.44–93.58 | |
Over-standard rate c (N) | 3.55 (12) | 0 (0) | 0 (0) | 1.16 (1) | 13.58 (11) | 0 (0) | 8.45 (12) | |
YC | Mean a | 30.16 | 29.07 | 14.41 | 35.54 | 41.28 | 22.66 | 40.37 |
Range b | 4.39–94.30 | 9.21–61.54 | 4.39–53.09 | 8.76–72.61 | 10.71–94.30 | 4.39–57.39 | 10.71–94.30 | |
Over-standard rate c (N) | 0.30(1) | 0(0) | 0 (0) | 0 (0) | 1.23(1) | 0 (0) | 0.70 (1) | |
XZ | Mean a | 44.08 | 45.48 | 31.37 | 46.05 | 52.77 | 37.04 | 50.86 |
Range b | 10.78–95.50 | 10.78–90.63 | 16.90–52.72 | 15.45–81.03 | 11.68–95.50 | 10.78–70.08 | 11.68–95.50 | |
Over-standard rate c (N) | 3.83 (3) | 3.26 (3) | 0 (0) | 1.15 (1) | 11.11 (9) | 0 (0) | 9.15 (13) | |
LL | Mean a | 44.42 | 44.42 | 43.13 | 42.96 | 47.22 | 42.06 | 47.65 |
Range b | 10.31–75.84 | 11.71–71.71 | 19.39–75.72 | 19.21–73.10 | 10.31–75.84 | 11.71–75.72 | 10.31–75.84 | |
Over-standard rate c (N) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) |
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Li, H.; Zhang, J.; Wen, B.; Huang, S.; Gao, S.; Li, H.; Zhao, Z.; Zhang, Y.; Fu, G.; Bai, J.; et al. Spatial-Temporal Distribution and Variation of NO2 and Its Sources and Chemical Sinks in Shanxi Province, China. Atmosphere 2022, 13, 1096. https://doi.org/10.3390/atmos13071096
Li H, Zhang J, Wen B, Huang S, Gao S, Li H, Zhao Z, Zhang Y, Fu G, Bai J, et al. Spatial-Temporal Distribution and Variation of NO2 and Its Sources and Chemical Sinks in Shanxi Province, China. Atmosphere. 2022; 13(7):1096. https://doi.org/10.3390/atmos13071096
Chicago/Turabian StyleLi, Hongyan, Jin Zhang, Biao Wen, Shidan Huang, Shuqin Gao, Hongyu Li, Zhixin Zhao, Yanru Zhang, Guo Fu, Jingai Bai, and et al. 2022. "Spatial-Temporal Distribution and Variation of NO2 and Its Sources and Chemical Sinks in Shanxi Province, China" Atmosphere 13, no. 7: 1096. https://doi.org/10.3390/atmos13071096