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Article

Impact of COVID-19 Lockdown on Air Pollutants in a Coastal Area of the Yangtze River Delta, China, Measured by a Low-Cost Sensor Package

by
Lang Chen
1,
Jingjing Li
2,
Xiaobing Pang
1,*,
Kangli Shi
1,
Jianmeng Chen
1,*,
Junliang Wang
1 and
Meng Xu
3
1
College of Environment, Zhejiang University of Technology, Hangzhou 310014, China
2
Shaoxing Ecological and Environmental Monitoring Center of Zhejiang Province, Shaoxing 312000, China
3
College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
*
Authors to whom correspondence should be addressed.
Atmosphere 2021, 12(3), 345; https://doi.org/10.3390/atmos12030345
Submission received: 23 January 2021 / Revised: 23 February 2021 / Accepted: 4 March 2021 / Published: 6 March 2021
(This article belongs to the Section Air Quality)
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Abstract

:
Ningbo is a major coastal city in the Yangtze River Delta region, China, with the largest cargo capacity in the world. We conducted a field campaign in Ningbo to measure the impact of the COVID-19 lockdown on air pollutants including NO2, O3 and CO from 21 January to 23 March 2020, using a home-made low-cost sensor package. The average concentrations of NO2, O3 and CO were observed to be 7.2, 37.5 and 648.5 ppb, respectively, during the lockdown. Compared with the previous year, the concentrations of NO2 and CO decreased by 63.1% and 6.9%, while the concentration of O3 increased by 37.9%. The significant reduction of NO2 concentration may be attributed to the reduced emissions of freighters and heavy trucks with lower port cargo throughput, which led to a decrease of NO concentration. The increase of O3 concentration was probably due to the lower titration of O3 by NO. After the lockdown, the concentrations of O3 and NO2 increased by 15.5% and 143.1%, respectively, compared with those during the lockdown. The temporal variations of the concentrations of NO2, O3 and CO measured by the sensor package were coincident with those obtained by the reference apparatus, which proves the sensor package to be suitable for air quality monitoring in field campaigns. This is the first time that a dramatic decrease in NO2 concentration in a coastal city due to a lockdown has been reported.

1. Introduction

The COVID-19 lockdown was enforced by the Chinese government to prevent the spread of the coronavirus at the beginning of 2020 and included measures such as closing-off management, restricting traffic and closing public places. Studying the impact of lockdowns on the world’s air pollution is an important scientific issue [1,2]. Air pollution was positively correlated with new confirmed cases, and the coronavirus spread by a further 5~7% as the air quality index increased by 10 units [3]. As a result of the lockdown, the air quality of the countries that enforced a lockdown significantly improved [4,5]. An unprecedented NO2 concentration decrease was observed over China, Korea, Western Europe and the U.S. during the lockdown [6]. The concentrations of SO2, NO2 and CO decreased in the Yangtze River Delta (YRD) region, China [7] and Wuhan, China [2], and the O3 concentration increased in Beijing, China [8] during the lockdown.
Other countries also implemented a lockdown to prevent the spread of the coronavirus. The concentrations of NO2 and CO were observed to decrease in Brazil [9,10] and India [11], and the O3 concentration increased in Southern European cities [1] and Brazil [12] during their lockdowns. It was found that the increase of O3 concentration was due to an increase in NMHC/NOx ratios in a volatile organic compound (VOC) controlled scenario [12], with less scavenging of HO2 [13] and a decrease of NO concentration [14]. The concentrations of NO2, CO and SO2 in some countries were observed to be decreased and the O3 concentration increased during the lockdown (Table 1). However, most research has focused on inland cities, and there are few studies dealing with large coastal cities.
Ningbo is a major coastal city in the YRD region of China. The shipping activities have increased significantly due to intensified international trade in recent years. Ningbo was the largest port in the world, with more than one billion tons of cargo transportation, in 2019 [22]. Ningbo is characterized by a large number of freighters, inland-water ships, heavy trucks and port equipment, which discharge a large number of ship-related air pollutants. The vast majority (95%) of the world’s ships use diesel as fuel. The quality of diesel used by ships is usually lower than that of diesel used by road vehicles; as a result, the ships emit much more pollutant emissions [23]. The emissions from freighters have caused significant increases in the concentrations of NO2 and SO2 in coastal areas [24,25]. It has been estimated that the global shipping emissions of SOx, NOx and CO in 2015 were 9.69 × 106 t, 2.09 × 107 t and 1.35 × 106 t, respectively [26]. The exhaust emissions from freighters and heavy trucks for cargo transportation were considered to be the major sources of local air pollution in Ningbo [27,28,29]. In recent years, the increase in the number of motor vehicles has resulted in frequent air pollution incidents [30].
We conducted a field campaign in Ningbo from 21 January to 23 March 2020 to study the impact of the COVID-19 lockdown on the air quality of the major coastal city using a home-made low-cost sensor package. The sensor package was designed for portable deployments in some field campaigns. The year 2019 is the closest year to 2020, and a comparison with 2019 can best reflect the impact of the COVID-19 lockdown on the air quality of Ningbo. At the same time, due to the late construction of the environmental monitoring station, only the data from 2019 were selected for comparison. We compared the temporal variations of the concentrations of NO2, O3 and CO during the lockdown with the same period of the previous year and discuss the possible reasons for the variations of air pollutants.

2. Materials and Methods

2.1. Sampling Site

The sampling site is located in an air quality monitoring station built near a coastal area of the YRD region of China, Ningbo Port (Figure 1).
The sampling site is located in Ningbo Petrochemical Economic and Technological Development Zone, a national chemical industrial park. The sampling site is located on a hilltop about 2 km away from the seaside port in Ningbo, which can be considered as an urban air quality monitor for ship-related air pollutants, industrial emissions and traffic exhaust emissions (e.g., heavy trucks). Ningbo covers an area of 9816 km2, with a total population of 8.542 million people as of 2019. Ningbo has a subtropical monsoon climate. In winter, Ningbo is generally influenced by high pressure from Mongolia with a prevailing northerly wind and cold air from Siberia continuously added to the south, with occasionally polluted air. The wind directions during the sampling period were mainly southeast wind and northeast wind, which were able to capture the emission of air pollutants from coastal ships. As shown in Table 2, the total number of days of northeast wind and southeast wind during the whole sampling period in 2020 was 36 days (http://www.weather.com.cn/ (accessed on 1 November 2020)). From 21 January to 23 March 2020, the daily average maximum temperature was 14.0 °C, and the daily average minimum temperature was 6.8 °C. From 1 February to 4 April 2019, the daily average maximum temperature was 13.2 °C, and the daily average minimum temperature was 6.7 °C. The temperature difference between the experimental period in 2020 and the same period in 2019 did not exceed 1 °C. Due to the particularity of the Chinese New Year period, 2020 and 2019 were compared according to the same lunar time.

2.2. Impact of COVID-19 on a Coastal Area of the YRD Region of China

From 21 January to 1 March 2020, Ningbo was placed in a lockdown in response to the public health emergency caused by COVID-19. The period from 21 January to 1 March was set as during the lockdown, and the period from 2 March to 23 March was set as after the lockdown. Figure 2a shows the changing trend of the number of COVID-19 cases in Ningbo (https://wsjkw.zj.gov.cn/ (accessed on 1 November 2020)). The first COVID-19 case was confirmed on 21 January and the last case was found on 20 February, with 157 cases in total in Ningbo. Travel intensities of Ningbo from 21 January to 15 March 2020 are shown in Figure 2b and were much lower than the previous year, with a reduction of 47% during the lockdown (https://qianxi.baidu.com/2020/ (accessed on 1 November 2020)), which led to great reductions in vehicular exhaust emissions.
The economic situation of Ningbo was significantly affected by the lockdown in the first quarter of 2020 (http://tjj.ningbo.gov.cn/ (accessed on 1 November 2020)) (Table 3).
The decline rates of most economic industries reached more than 10% compared with those of the previous year, which led to a great reduction of exhaust emissions from industries and motor vehicles. The cargo throughput of Ningbo Port in the first quarter of 2020 was significantly decreased compared with that of the previous year (http://www.nbport.com.cn/gfww/ (accessed on 1 November 2020)) (Table 4), which resulted in significantly lower exhaust emissions from ship-related air pollutants (e.g., freighters and heavy trucks).

2.3. Experimental Instruments

In this study, a sensor package that consisted of electrochemical sensors was deployed. The information of the sensor package and calibration methods and detailed linear comparison results of the sensor package and the reference apparatus can be seen in our previous studies [31]. The reference apparatuses selected were a CO monitor (48i, Thermo Fisher Scientific, Waltham, MA, USA), NO2 monitor (42i, Thermo Fisher Scientific, Waltham, MA, USA) and O3 monitor (49i, Thermo Fisher Scientific, Waltham, MA, USA).
The inlet airflow was divided into two channels, of which one was for the sensor package and the other was for the reference apparatus. The data acquisition frequency of the sensor package and the reference apparatus was 5 s, and the gas flow rate was 0.6 L/min. The sampling air was heated through a sampling pipe heated and kept at 40 °C.

3. Results and Discussion

3.1. Comparison between Sensor Package and Reference Apparatus

R2 refers to the consistency between the sensor package and the reference apparatus. The closer R2 is to 1, the better the comparison results between the two instruments are. To verify the reliability of the sensor, five concentration gradients were compared between each sensor and the standard gas produced by the reference apparatus. The linear fitting equation lines and R2 of the sensor package and the standard gas are shown in Figure 3, which confirm the linear responses of the sensor package to the standard gases.
It can be seen from Figure 3 that the sensor package showed good consistency with the standard gas, as the R2 is close to 1, and the sensor package was calibrated with the data in Figure 3. Rain reduces the concentration of air pollutants, increases the humidity in the air and affects the detection accuracy of the sensor package and the reference apparatus. From 18 to 27 February 2020, the weather in Ningbo was sunny or cloudy. To reduce the impact of weather on the detection during the lockdown, the data from this period were selected for comparison. The data measured by the sensor package and the reference apparatus from 18 to 27 February 2020 are compared and shown in Figure 4.
The temporal variations of the concentrations of O3 and NO2 obtained by the sensor package were consistent with those obtained by the reference apparatus with correlation coefficients (R2) of 0.8957 (n = 240) for O3 and 0.6235 (n = 240) for NO2. The trend of CO sensor data was consistent with that of the reference apparatus overall, except for several minor differences. The mean biases of the reference apparatus and the sensor package for CO concentration were 120 ppb and 165 ppb, respectively. The mean biases of the reference apparatus and the sensor package for O3 concentration were 8 ppb and 13 ppb, respectively, while those for NO2 concentration were 2 ppb and 2 ppb, respectively, and those for CO concentration were 120 ppb and 165 ppb, respectively.

3.2. Impact on Nitrogen Dioxide (NO2)

During the lockdown, the average concentration of NO2 was 7.2 ppb, which was 63.1% lower than that of the same period in the previous year. After the lockdown, the average concentration of NO2 increased to 17.5 ppb, which was lower than that of the previous year, with a decrease of 6.7 ppb, but was higher than that during the lockdown, with an increase of 10.3 ppb (Table 5).
The NO2 concentration decreased slightly at the beginning of the lockdown (Figure 5a). During the Chinese New Year, the economic production activities decreased, followed by a reduction of air pollutants. Therefore, during the Chinese New Year in 2019 and 2020, the concentration of NO2 was at a low level, and the impact of the lockdown on the concentration of NO2 was not obvious. Five days after the Chinese New Year, economic production activities gradually began to increase, and NO2 concentration increased significantly in 2019, while in the same period of 2020, the NO2 concentration remained at a low level. Five to 35 days after the Chinese New Year, the highest concentration of NO2 was equal to the lowest level of the previous year (Figure 5b). During this period, the number of daily new confirmed cases reached the maximum of 27 on 3 February, the cumulative number of confirmed cases reached the maximum of 157 on 20 February, and the difference between the travel intensity on 16 February and the previous year reached the maximum of 4.02 (Figure 2). As can be seen from Figure 5b, with the increase of the total number of confirmed cases, the difference between the concentration of NO2 during the lockdown and that of the previous year was expanding. Nineteen days after the Chinese New Year, the difference of the NO2 concentration in 2020 and 2019 reached its maximum of 56 ppb during the lockdown, with a decrease of 94.9% compared with the previous year when the total number of confirmed COVID-19 cases reached 153. After the lockdown, Ningbo began to resume economic production, NO2 emissions increased, and NO2 concentration increased significantly and reached the same level as the previous year (Figure 5c).
NO2 in ambient air mainly comes from the combustion of fossil fuels, such as ship and vehicular exhausts and industrial activities [32,33]. NO2 concentration is considered to be an indicator of anthropogenic pollution as it mainly arises from anthropogenic sources and not biogenic emission [34,35]. According to the data in Figure 2b, Table 3 and Table 4, compared with the previous year, the travel intensity, economic production activities and port cargo throughput of Ningbo during the lockdown decreased significantly. The significant decrease of NO2 concentration can be attributed to the reduction of emissions from ship-related air pollutants (e.g., freighters and heavy trucks), local vehicle exhaust and industrial emissions during the lockdown. The decline of NO2 concentration was observed in almost all cities that experienced a lockdown, but the decline rate of NO2 concentration in many cities was generally less than 60%, except for in Wuhan, China (Table 1). It can be inferred that the lockdown had a great impact on the emission of NO2 in coastal cities.

3.3. Impact on Ozone (O3)

Unlike the dramatic decrease in NO2 concentration, an increase of O3 concentration was not obvious; this is dependent on the VOC–NOX regime. During the lockdown, the average concentration of O3 was 37.5 ppb, compared with 27.2 ppb in the same period in the previous year, with an increase of 10.3 ppb and 37.9% (Table 5). After the lockdown, the average concentration of O3 increased to 43.3 ppb, which was 11.1 ppb higher than the previous year and 5.8 ppb higher than that during the lockdown (Table 5).
When the lockdown was first implemented, the O3 concentration increased from 0 to 15 ppb (Figure 5e). Starting from 3 days after the Chinese New Year, the O3 concentration increased compared with that of the previous year and reached the maximum difference of 62 ppb after 7 days of the Chinese New Year (Figure 5f). It can be seen from Figure 5b,f that, after the Chinese New Year in 2019, the concentration of NO2 increased significantly, while the concentration of O3 decreased significantly. However, the opposite phenomenon appeared during the lockdown in 2020. The increase of O3 concentration should be related to the decrease of NO2. NO2 first dissociates into NO and O(3P) under sufficient solar radiation. A lower concentration of NO reacts with less O3 in ambient air, which results in the accumulation of the concentration of O3 in ambient air.
NO2 + hv (λ ≤ 430 nm) → NO + O(3P)
O(3P) + O2 → O3
NO + O3 → NO2 + O2
After the lockdown, the O3 concentration was almost the same as that of the previous year (Figure 5g). Until 51 days after the Chinese New Year, the O3 concentration increased significantly (Figure 5h), and the corresponding NO2 concentration was low (Figure 5d). The sudden increase of O3 concentration may have been due to the low concentration of NO2 in the environment. The changes of O3 concentration and NOx concentrations in Ningbo showed the opposite trend [36].
The formation of O3 in ambient air is mainly influenced by NOx concentrations, VOC concentrations and meteorological parameters [37]. As a result of the lockdown, the NO2 concentration in Ningbo decreased by 63.1%. The increase of O3 in Figure 5f is related to the decrease of NO2 in Figure 5b. It was found that the main reason for the significant increase of O3 concentration during the lockdown was the substantial reduction of NO2 concentration [1,10,38].

3.4. Impact on Carbon Monoxide (CO)

During the lockdown, the average concentration of CO was 648.5 ppb, with a decrease of 48.1 ppb compared with the level of 696.6 ppb for the previous year. After the lockdown, the average concentration of CO decreased to 524.9 ppb, which was 123.6 ppb lower than that during the lockdown (Table 5). Before the Chinese New Year, the concentration of CO was relatively high. After the Chinese New Year of 2019, the concentration of CO showed a rising trend. During the lockdown in 2020, the concentration of CO showed a declining trend (Figure 5i). This was due to the continuous reduction of CO emissions due to the lockdown. Until 15 to 27 days after the Chinese New Year, the concentration of CO was significantly lower than that of the previous year. At this time, the lockdown entered the most stringent period. After the lockdown, the concentration of CO did not increase significantly as was the case for the concentration of NO2 with the recovery of economic production activities.
CO mainly originates from vehicle exhaust emissions [39], biomass and fossil fuel combustion [40,41] and industrial activities [42]. The environmental background value of CO is relatively high, and the lifetime of CO is longer than that of NO2. Therefore, the decline of CO during the lockdown was not much less than that of NO2. As a result of the lockdown, the CO concentration in many countries decreased during the lockdown (Table 1). The reduction of CO concentration can be attributed to the reduction of emissions from ship-related air pollutants, vehicle exhaust and industrial emissions.

4. Conclusions

During the COVID-19 lockdown, the concentrations of NO2 and CO decreased by 63.1% and 6.9% compared with those of 2019 in a major coastal city in the YRD, China. The dramatic reductions of ambient NO2 concentration were only observed in the coastal city, which was mainly attributed to the reduction of ship-related air pollutants except for industrial and vehicle exhaust emissions. The lockdown resulted in an increase of 37.9% of the O3 concentration, which was closely related to the sharp decrease of NO2 concentration in a VOC-controlled scenario. The concentrations of NO2 and O3 were greatly affected by the lockdown, while CO concentration was less affected due to its high background value and long lifetime. After the lockdown, with the recovery of the shipping industry and other economic activities, the concentrations of NO2 and O3 quickly reached the same level in one month as the previous year, which confirms that the shipping exhausts were the main sources of these pollutants in the major coastal city of Ningbo. This study provides helpful scientific information on air pollution control policies in coastal cities. The sensor package showed good consistency with the reference apparatus in ambient air measurements, providing an alternative low-cost but accurate device for field campaigns and air-quality monitoring stations without a regular power supply in harsh wild conditions.

Author Contributions

Conceptualization, L.C. and X.P.; methodology, J.W.; software, J.L.; validation, K.S., L.C. and J.C.; investigation, M.X.; data curation, K.S.; writing—original draft preparation, L.C.; writing—review and editing, X.P.; project administration, X.P.; funding acquisition, X.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development of China (2017YFC0209701 and 2018YFC0214100), the Natural Sciences Foundation of Zhejiang Province, China (LZ20D050002) and the National Natural Science Foundation of China (41977304).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data that were used in this article have not open access. These data can be accessible to university scientific staff with the permission of the head of the laboratory.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Sicard, P.; De Marco, A.; Agathokleous, E.; Feng, Z.; Xu, X.; Paoletti, E.; Rodriguez, J.J.D.; Calatayud, V. Amplified ozone pollution in cities during the COVID-19 lockdown. Sci. Total Environ. 2020, 735, 139542. [Google Scholar] [CrossRef]
  2. Xu, K.; Cui, K.; Young, L.; Hsieh, Y.; Wang, Y.; Zhang, J.; Wan, S. Impact of the COVID-19 Event on Air Quality in Central China. Aerosol Air Qual. Res. 2020, 20, 915–929. [Google Scholar] [CrossRef] [Green Version]
  3. Zhang, Z.; Xue, T.; Jin, X. Effects of meteorological conditions and air pollution on COVID-19 transmission: Evidence from 219 Chinese cities. Sci. Total Environ. 2020, 741, 140244. [Google Scholar] [CrossRef]
  4. Ghahremanloo, M.; Lops, Y.; Choi, Y.; Mousavinezhad, S. Impact of the COVID-19 outbreak on air pollution levels in East Asia. Sci. Total Environ. 2021, 754, 142226. [Google Scholar] [CrossRef] [PubMed]
  5. Wang, Q.; Su, M. A preliminary assessment of the impact of COVID-19 on environment—A case study of China. Sci. Total Environ. 2020, 728, 138915. [Google Scholar] [CrossRef]
  6. Bauwens, M.; Compernolle, S.; Stavrakou, T.; Muller, J.F.; van Gent, J.; Eskes, H.; Levelt, P.F.; van der A, R.; Veefkind, J.P.; Vlietinck, J.; et al. Impact of coronavirus outbreak on NO2 pollution assessed using TROPOMI and OMI observations. Geophys. Res. Lett. 2020, 47. [Google Scholar] [CrossRef]
  7. Li, L.; Li, Q.; Huang, L.; Wang, Q.; Zhu, A.; Xu, J.; Liu, Z.; Li, H.; Shi, L.; Li, R.; et al. Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation. Sci. Total Environ. 2020, 732, 139282. [Google Scholar] [CrossRef] [PubMed]
  8. Pei, Z.; Han, G.; Ma, X.; Su, H.; Gong, W. Response of major air pollutants to COVID-19 lockdowns in China. Sci. Total Environ. 2020, 743, 140879. [Google Scholar] [CrossRef] [PubMed]
  9. Nakada, L.Y.K.; Urban, R.C. COVID-19 pandemic: Impacts on the air quality during the partial lockdown in São Paulo state, Brazil. Sci. Total Environ. 2020, 730, 139087. [Google Scholar] [CrossRef]
  10. Dantas, G.; Siciliano, B.; França, B.B.; da Silva, C.M.; Arbilla, G. The impact of COVID-19 partial lockdown on the air quality of the city of Rio de Janeiro, Brazil. Sci. Total Environ. 2020, 729, 139085. [Google Scholar] [CrossRef] [PubMed]
  11. Mahato, S.; Pal, S.; Ghosh, K.G. Effect of lockdown amid COVID-19 pandemic on air quality of the megacity Delhi, India. Sci. Total Environ. 2020, 730, 139086. [Google Scholar] [CrossRef] [PubMed]
  12. Siciliano, B.; Dantas, G.; da Silva, C.M.; Arbilla, G. Increased ozone levels during the COVID-19 lockdown: Analysis for the city of Rio de Janeiro, Brazil. Sci. Total Environ. 2020, 737, 139765. [Google Scholar] [CrossRef]
  13. Wang, Y.; Yuan, Y.; Wang, Q.; Liu, C.; Zhi, Q.; Cao, J. Changes in air quality related to the control of coronavirus in China: Implications for traffic and industrial emissions. Sci. Total Environ. 2020, 731, 139133. [Google Scholar] [CrossRef] [PubMed]
  14. Tobias, A.; Carnerero, C.; Reche, C.; Massague, J.; Via, M.; Minguillon, M.C.; Alastuey, A.; Querol, X. Changes in air quality during the lockdown in Barcelona (Spain) one month into the SARS-CoV-2 epidemic. Sci. Total Environ. 2020, 726, 4. [Google Scholar] [CrossRef] [PubMed]
  15. Xiang, J.; Austin, E.; Gould, T.; Larson, T.; Shirai, J.; Liu, Y.; Marshall, J.; Seto, E. Impacts of the COVID-19 responses on traffic-related air pollution in a Northwestern US city. Sci. Total Environ. 2020, 747, 141325. [Google Scholar] [CrossRef]
  16. Liu, Q.; Harris, J.T.; Chiu, L.S.; Sun, D.; Houser, P.R.; Yu, M.; Duffy, D.Q.; Little, M.M.; Yang, C. Spatiotemporal impacts of COVID-19 on air pollution in California, USA. Sci. Total Environ. 2021, 750, 141592. [Google Scholar] [CrossRef]
  17. Singh, V.; Singh, S.; Biswal, A.; Kesarkar, A.P.; Mor, S.; Ravindra, K. Diurnal and temporal changes in air pollution during COVID-19 strict lockdown over different regions of India. Environ. Pollut. 2020, 266, 115368. [Google Scholar] [CrossRef] [PubMed]
  18. Otmani, A.; Benchrif, A.; Tahri, M.; Bounakhla, M.; Chakir, E.M.; El Bouch, M.; Krombi, M.h. Impact of Covid-19 lockdown on PM10, SO2 and NO2 concentrations in Salé City (Morocco). Sci. Total Environ. 2020, 735, 139541. [Google Scholar] [CrossRef] [PubMed]
  19. Ju, M.J.; Oh, J.; Choi, Y.-H. Changes in air pollution levels after COVID-19 outbreak in Korea. Sci. Total Environ. 2021, 750, 141521. [Google Scholar] [CrossRef]
  20. Kanniah, K.D.; Zaman, N.; Kaskaoutis, D.G.; Latif, M.T. COVID-19’s impact on the atmospheric environment in the Southeast Asia region. Sci. Total Environ. 2020, 736. [Google Scholar] [CrossRef]
  21. Kerimray, A.; Baimatova, N.; Ibragimova, O.P.; Bukenov, B.; Kenessov, B.; Plotitsyn, P.; Karaca, F. Assessing air quality changes in large cities during COVID-19 lockdowns: The impacts of traffic-free urban conditions in Almaty, Kazakhstan. Sci. Total Environ. 2020, 730, 139179. [Google Scholar] [CrossRef]
  22. Yuan, Q.; Teng, X.; Tu, S.; Feng, B.; Wu, Z.; Xiao, H.; Cai, Q.; Zhang, Y.; Lin, Q.; Liu, Z.; et al. Atmospheric fine particles in a typical coastal port of Yangtze River Delta. J. Environ. Sci. 2020, 98, 62–70. [Google Scholar] [CrossRef]
  23. Cullinane, K.; Bergqvist, R. Emission control areas and their impact on maritime transport. Transp. Res. Part D Transp. Environ. 2014, 28, 1–5. [Google Scholar] [CrossRef]
  24. Marelle, L.; Thomas, J.L.; Raut, J.C.; Law, K.S.; Jalkanen, J.P.; Johansson, L.; Roiger, A.; Schlager, H.; Kim, J.; Reiter, A.; et al. Air quality and radiative impacts of Arctic shipping emissions in the summertime in northern Norway: From the local to the regional scale. Atmos. Chem. Phys. 2016, 16, 2359–2379. [Google Scholar] [CrossRef] [Green Version]
  25. Xue, R.B.; Wang, S.S.; Li, D.R.; Zou, Z.; Chan, K.L.; Valks, P.; Saiz-Lopez, A.; Zhou, B. Spatio-temporal variations in NO2 and SO2 over Shanghai and Chongming Eco-Island measured by Ozone Monitoring Instrument (OMI) during 2008–2017. J. Clean. Prod. 2020, 258, 14. [Google Scholar] [CrossRef]
  26. Johansson, L.; Jalkanen, J.-P.; Kukkonen, J. Global assessment of shipping emissions in 2015 on a high spatial and temporal resolution. Atmos. Environ. 2017, 167, 403–415. [Google Scholar] [CrossRef]
  27. Mo, Z.; Shao, M.; Lu, S.; Niu, H.; Zhou, M.; Sun, J. Characterization of non-methane hydrocarbons and their sources in an industrialized coastal city, Yangtze River Delta, China. Sci. Total Environ. 2017, 593–594, 641–653. [Google Scholar] [CrossRef]
  28. Wang, X.; Shen, Y.; Lin, Y.; Pan, J.; Zhang, Y.; Louie, P.K.K.; Li, M.; Fu, Q. Atmospheric pollution from ships and its impact on local air quality at a port site in Shanghai. Atmos. Chem. Phys. 2019, 19, 6315–6330. [Google Scholar] [CrossRef] [Green Version]
  29. Griffith, S.M.; Huang, W.S.; Lin, C.C.; Chen, Y.C.; Chang, K.E.; Lin, T.H.; Wang, S.H.; Lin, N.H. Long-range air pollution transport in East Asia during the first week of the COVID-19 lockdown in China. Sci. Total Environ. 2020, 741, 140214. [Google Scholar] [CrossRef] [PubMed]
  30. Hua, Y.; Cheng, Z.; Wang, S.; Jiang, J.; Chen, D.; Cai, S.; Fu, X.; Fu, Q.; Chen, C.; Xu, B.; et al. Characteristics and source apportionment of PM2.5 during a fall heavy haze episode in the Yangtze River Delta of China. Atmos. Environ. 2015, 123, 380–391. [Google Scholar] [CrossRef]
  31. Pang, X.; Chen, L.; Shi, K.; Wu, F.; Chen, J.; Fang, S.; Wang, J.; Xu, M. A lightweight low-cost and multipollutant sensor package for aerial observations of air pollutants in atmospheric boundary layer. Sci. Total Environ. 2020. [Google Scholar] [CrossRef]
  32. Zyrichidou, I.; Κoukouli, M.E.; Balis, D.; Markakis, K.; Poupkou, A.; Katragkou, E.; Kioutsioukis, I.; Melas, D.; Boersma, K.F.; van Roozendael, M. Identification of surface NOx emission sources on a regional scale using OMI NO2. Atmos. Environ. 2015, 101, 82–93. [Google Scholar] [CrossRef]
  33. Gerstenberger, M.; Listl, G. Impact analysis of changes in passenger vehicle fleet composition to reduce the NO2 immissions. Transp. Res. Procedia 2019, 41, 708–721. [Google Scholar] [CrossRef]
  34. Lin, J.T.; McElroy, M.B. Detection from space of a reduction in anthropogenic emissions of nitrogen oxides during the Chinese economic downturn. Atmos. Chem. Phys. 2011, 11, 8171–8188. [Google Scholar] [CrossRef] [Green Version]
  35. Zhang, R.X.; Wang, Y.H.; Smeltzer, C.; Qu, H.; Koshak, W.; Boersma, K.F. Comparing OMI-based and EPA AQS in situ NO2 trends: Towards understanding surface NOx emission changes. Atmos. Meas. Tech. 2018, 11, 3955–3967. [Google Scholar] [CrossRef] [Green Version]
  36. Tong, L.; Zhang, H.; Yu, J.; He, M.; Xu, N.; Zhang, J.; Qian, F.; Feng, J.; Xiao, H. Characteristics of surface ozone and nitrogen oxides at urban, suburban and rural sites in Ningbo, China. Atmos. Res. 2017, 187, 57–68. [Google Scholar] [CrossRef]
  37. Wang, T.; Xue, L.; Brimblecombe, P.; Lam, Y.F.; Li, L.; Zhang, L. Ozone pollution in China: A review of concentrations, meteorological influences, chemical precursors, and effects. Sci. Total Environ. 2017, 575, 1582–1596. [Google Scholar] [CrossRef]
  38. Jain, S.; Sharma, T. Social and travel lockdown impact considering coronavirus disease (COVID-19) on air quality in megacities of India: Present Benefits, Future Challenges and Way Forward. Aerosol Air Qual. Res. 2020, 20, 1222–1236. [Google Scholar] [CrossRef]
  39. Payus, C.M.; Vasu Thevan, A.T.; Sentian, J. Impact of school traffic on outdoor carbon monoxide levels. City Environ. Interact. 2019, 4, 100032. [Google Scholar] [CrossRef]
  40. Hao, H.; Chow, C.L.; Lau, D. Carbon monoxide release mechanism in cellulose combustion using reactive forcefield. Fuel 2020, 269, 117422. [Google Scholar] [CrossRef]
  41. Yarragunta, Y.; Srivastava, S.; Mitra, D.; Le Flochmoën, E.; Barret, B.; Kumar, P.; Chandola, H.C. Source attribution of carbon monoxide and ozone over the Indian subcontinent using MOZART-4 chemistry transport model. Atmos. Res. 2019, 227, 165–177. [Google Scholar] [CrossRef]
  42. Liu, S.; Fang, S.; Liang, M.; Sun, W.; Feng, Z. Temporal patterns and source regions of atmospheric carbon monoxide at two background stations in China. Atmos. Res. 2019, 220, 169–180. [Google Scholar] [CrossRef]
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Figure 1. Location of sampling site (121°32′ E, 30°30′ N) on the map, which is located in the north of Ningbo.
Figure 1. Location of sampling site (121°32′ E, 30°30′ N) on the map, which is located in the north of Ningbo.
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Atmosphere 12 00345 g002 550
Figure 2. (a) Cumulative confirmed COVID-19 cases and daily new confirmed cases in Ningbo. The shaded area shows the period during the lockdown. (b) Comparison of travel intensity of Ningbo between 2020 and 2019. The Chinese New Year in the figure shows that of 2020 and 2019, which are compared according to the Chinese lunar period. The x-axis is the number of days relative to the Chinese New Year. Travel intensity refers to the indexation result of the ratio of the number of people traveling in the city to the resident urban population. The shaded area shows the period during the lockdown.
Figure 2. (a) Cumulative confirmed COVID-19 cases and daily new confirmed cases in Ningbo. The shaded area shows the period during the lockdown. (b) Comparison of travel intensity of Ningbo between 2020 and 2019. The Chinese New Year in the figure shows that of 2020 and 2019, which are compared according to the Chinese lunar period. The x-axis is the number of days relative to the Chinese New Year. Travel intensity refers to the indexation result of the ratio of the number of people traveling in the city to the resident urban population. The shaded area shows the period during the lockdown.
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Atmosphere 12 00345 g003 550
Figure 3. Calibration curves of the sensor package responses to the standard gases at different concentrations with their linear correlation coefficients (R2): 0.9979 for O3, 0.9942 for NO2 and 0.9970 for CO.
Figure 3. Calibration curves of the sensor package responses to the standard gases at different concentrations with their linear correlation coefficients (R2): 0.9979 for O3, 0.9942 for NO2 and 0.9970 for CO.
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Atmosphere 12 00345 g004 550
Figure 4. Comparison of temporal variations of the concentrations of O3, NO2 and CO detected by the sensor package and the reference apparatus from 18 to 27 February 2020. The data depicted by the red line come from the sensor package, and the data depicted by the black line come from the reference apparatus.
Figure 4. Comparison of temporal variations of the concentrations of O3, NO2 and CO detected by the sensor package and the reference apparatus from 18 to 27 February 2020. The data depicted by the red line come from the sensor package, and the data depicted by the black line come from the reference apparatus.
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Atmosphere 12 00345 g005 550
Figure 5. Comparison of temporal variations of the concentrations of NO2, O3 and CO between 2020 and 2019. The Chinese New Year in the figure refers to 2020 and 2019, which are compared according to the Chinese lunar period. The x-axis is the number of days relative to the Chinese New Year. The data depicted by the black line in 2019 come from the reference apparatus, and the data depicted by the red line in 2020 come from the sensor package. The shaded area shows the period during the lockdown. (a) The NO2 concentration decreased slightly at the beginning of the lockdown; (b) Five to 35 days after the Chinese New Year, the highest concentration of NO2 was equal to the lowest level of the previous year; (c) The NO2 concentration increased significantly and reached the same level as the previous year; (d) In Figure 5h, the concentration of O3 increased significantly and the corresponding NO2 concentration was low; (e) When the lockdown was first implemented, the O3 concentration increased from 0 to 15 ppb; (f) Starting from 3 days after the Chinese New Year, the O3 concentration increased compared with that of the previous year and reached the maximum difference of 62 ppb after 7 days of the Chinese New Year; (g) After the lockdown, the O3 concentration was almost the same as that of the previous year; (h) Until 51 days after the Chinese New Year, the O3 concentration increased significantly; (i) After the Chinese New Year of 2019, the concentration of CO showed a rising trend. During the lockdown in 2020, the concentration of CO showed a declining trend.
Figure 5. Comparison of temporal variations of the concentrations of NO2, O3 and CO between 2020 and 2019. The Chinese New Year in the figure refers to 2020 and 2019, which are compared according to the Chinese lunar period. The x-axis is the number of days relative to the Chinese New Year. The data depicted by the black line in 2019 come from the reference apparatus, and the data depicted by the red line in 2020 come from the sensor package. The shaded area shows the period during the lockdown. (a) The NO2 concentration decreased slightly at the beginning of the lockdown; (b) Five to 35 days after the Chinese New Year, the highest concentration of NO2 was equal to the lowest level of the previous year; (c) The NO2 concentration increased significantly and reached the same level as the previous year; (d) In Figure 5h, the concentration of O3 increased significantly and the corresponding NO2 concentration was low; (e) When the lockdown was first implemented, the O3 concentration increased from 0 to 15 ppb; (f) Starting from 3 days after the Chinese New Year, the O3 concentration increased compared with that of the previous year and reached the maximum difference of 62 ppb after 7 days of the Chinese New Year; (g) After the lockdown, the O3 concentration was almost the same as that of the previous year; (h) Until 51 days after the Chinese New Year, the O3 concentration increased significantly; (i) After the Chinese New Year of 2019, the concentration of CO showed a rising trend. During the lockdown in 2020, the concentration of CO showed a declining trend.
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Table
Table 1. Changes in air pollutants caused by lockdowns in cities or countries.
Table 1. Changes in air pollutants caused by lockdowns in cities or countries.
City, CountryO3NO2SO2CO
Ningbo, China+38%−63% -−7%
Wuhan, China [1,4]+36%−83% −71% −4%
Washington, USA [15]-−29% -−17%
California, USA [16]-−38% -−49%
São Paulo, Brazil [9]+30%−54% -−65%
Delhi, India [11]-−53% -−30%
India [17]-−30~70% -−20~40%
Salé, Morocco [18]-−96% −49% -
Seoul, Korea [4]-−33% --
Korea [19]-−20% -−17%
Tokyo, Japan [4]-−19% --
Malaysia [20]-−63~64%−9~20% −25~31%
Almaty, Kazakhstan [21]+15%−35%-−49%
Southern European cities [1]+2~27%---
Barcelona, Spain [14]+33~57%−45~51%--
Table
Table 2. The number of days with different wind directions during the sampling period in 2020 and the same period in 2019. The total number of experimental days was 63 days.
Table 2. The number of days with different wind directions during the sampling period in 2020 and the same period in 2019. The total number of experimental days was 63 days.
YearNortheast WindSoutheast Wind Northwest Wind Southwest Wind
20201620252
20191823211
Table
Table 3. The economic indicators of Ningbo in the first quarter of 2020 compared with those in the first quarter of 2019.
Table 3. The economic indicators of Ningbo in the first quarter of 2020 compared with those in the first quarter of 2019.
Economic Indicators (Unit)First-Quarter,
2019		First-Quarter,
2020		Variation in %
Electricity consumption of the whole city (100 million kwh)182.1152.6−16.2%
Industrial power consumption (100 million kwh)124.4102.0−18.0%
City’s GDP (RMB 100 million)2649.22463.8−7.0%
Total import and export (RMB 100 million)2053.71842.2−10.3%
Total retail sales of consumer goods (RMB 100 million)935.2806.1−13.8%
Industrial added value * (RMB 100 million)909.5802.2−11.8%
Industrial sales value * (RMB 100 million)4040.13304.8−18.2%
Industrial energy consumption * (10,000 tons of standard coal)687.3637.8−7.2%
Road freight volume (10,000 tons)7131.66233.0−12.6%
Waterway freight volume (10,000 tons)7176.85691.2−20.7%
Cargo throughput of civil aviation (10,000 tons)4.82.9−39.6%
Passenger traffic volume (ten thousand people)2764.71183.3−57.2%
* Industrial enterprises whose annual main business income is more than RMB 20 million.
Table
Table 4. Statistics of the container throughput and cargo throughput of Ningbo Zhoushan Port Co., Ltd. (Ningbo, China) from January to March 2020 and compared with those from January to March 2019.
Table 4. Statistics of the container throughput and cargo throughput of Ningbo Zhoushan Port Co., Ltd. (Ningbo, China) from January to March 2020 and compared with those from January to March 2019.
MonthContainer Throughput (10,000 TEUs *), 2019Container Throughput (10,000 TEUs), 2020Variation in %
January276267−3.3%
February204162−20.6%
March234228−2.6%
Cargo throughput (10,000 tons), 2019Cargo throughput (10,000 tons), 2020Variation in %
January73687154−2.9%
February58735397−8.1%
March69016266−9.2%
* TEUs: 20 feet equivalent units.
Table
Table 5. The comparison of the concentrations of NO2, O3 and CO between 2020 and 2019. The years 2020 and 2019 were compared according to the same lunar period. The period from 21 January to 1 March in 2020 was during the lockdown, and the period from 2 March to 23 March in 2020 was after the lockdown.
Table 5. The comparison of the concentrations of NO2, O3 and CO between 2020 and 2019. The years 2020 and 2019 were compared according to the same lunar period. The period from 21 January to 1 March in 2020 was during the lockdown, and the period from 2 March to 23 March in 2020 was after the lockdown.
Air Pollutants 1 February to 13 March 201921 January to 1 March 2020
During the Lockdown		DifferenceVariation in %
NO2 (ppb)19.57.2−12.3−63.1%
O3 (ppb)27.237.5+10.3+37.9%
CO (ppb)696.6648.5−48.1−6.9%
Air Pollutants14 March to 4 April 20192 March to 23 March 2020
After the Lockdown		DifferenceVariation in %
NO2 (ppb)24.217.5−6.7−27.7%
O3 (ppb)32.243.3+11.1+34.5%
CO (ppb)635.9524.9−111.0−17.5%
Air PollutantsDuring the Lockdown, 2020After the Lockdown, 2020DifferenceVariation in %
NO2 (ppb)7.217.5+10.3+143.1%
O3 (ppb)37.543.3+5.8+15.5%
CO (ppb)648.5524.9−123.6−19.1%
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Chen, L.; Li, J.; Pang, X.; Shi, K.; Chen, J.; Wang, J.; Xu, M. Impact of COVID-19 Lockdown on Air Pollutants in a Coastal Area of the Yangtze River Delta, China, Measured by a Low-Cost Sensor Package. Atmosphere 2021, 12, 345. https://doi.org/10.3390/atmos12030345

AMA Style

Chen L, Li J, Pang X, Shi K, Chen J, Wang J, Xu M. Impact of COVID-19 Lockdown on Air Pollutants in a Coastal Area of the Yangtze River Delta, China, Measured by a Low-Cost Sensor Package. Atmosphere. 2021; 12(3):345. https://doi.org/10.3390/atmos12030345

Chicago/Turabian Style

Chen, Lang, Jingjing Li, Xiaobing Pang, Kangli Shi, Jianmeng Chen, Junliang Wang, and Meng Xu. 2021. "Impact of COVID-19 Lockdown on Air Pollutants in a Coastal Area of the Yangtze River Delta, China, Measured by a Low-Cost Sensor Package" Atmosphere 12, no. 3: 345. https://doi.org/10.3390/atmos12030345

APA Style

Chen, L., Li, J., Pang, X., Shi, K., Chen, J., Wang, J., & Xu, M. (2021). Impact of COVID-19 Lockdown on Air Pollutants in a Coastal Area of the Yangtze River Delta, China, Measured by a Low-Cost Sensor Package. Atmosphere, 12(3), 345. https://doi.org/10.3390/atmos12030345

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