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Article

Application of Mobile Monitoring to Study Characteristics of Air Pollution in Typical Areas of the Yangtze River Delta Eco-Green Integration Demonstration Zone, China

by
Xinran Fu
1,
Qixin Cai
1,
Yitao Yang
1,
Yu Xu
1,
Fanghong Zhao
1,
Jie Yang
1,
Liping Qiao
2,
Lan Yao
1,2,3,* and
Weiyue Li
1
1
Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station, School of Environmental and Geological Sciences, Shanghai Normal University, Shanghai 200234, China
2
State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
3
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Shanghai 200433, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(1), 205; https://doi.org/10.3390/su15010205
Submission received: 1 December 2022 / Revised: 19 December 2022 / Accepted: 20 December 2022 / Published: 23 December 2022
(This article belongs to the Special Issue Aerosols and Air Pollution)
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Abstract

:
Mobile observation improves the accuracy and coverage of environmental monitoring, and can locate and track pollution sources. We conducted mobile monitoring to obtain real-time atmospheric pollutants (PM2.5, PM10, SO2, NO2, CO and O3) in typical areas, which included a country park and a tourist attraction featuring an ancient town in the Yangtze River Delta Eco-Green Integrated Development Demonstration Zone (Demonstration Zone), China. Results show that the concentrations of the six key pollutants in the ancient town were usually higher than that in the country park, due to high intensity of anthropogenic emissions. Pollutants including PM2.5, PM10, SO2 and CO in the ancient town during weekends were higher than that during weekdays, whereas pollutants in the country park presented no difference during weekdays and weekends. Morphology analysis of individual particles by scanning electron microscopy detected abundant soot from fresh emissions and atmospheric aging in the two areas. Agricultural irrigation, powered by diesel combustion, was identified as an emission source in the country park. Open-air cooking, coal combustion for cooking and the frequent redecoration of stores were emission sources in the ancient town. Environmentally friendly agricultural irrigation ways and cleaner cooking fuels were suggested to further improve air quality in the Demonstration Zone.

1. Introduction

The Yangtze River Delta is one of the regions with the most active economic development, the highest degree of openness and the strongest innovation ability in China, and plays a pivotal role in the national economy. For example, the Yangtze River Delta region only covers 4% of the national area, but accounts for 16.7% of the domestic population and contributes a quarter of the national GDP. In November 2018, the integrated regional development of the Yangtze River Delta region officially rose to a national strategy. In 2019, the National State Council approved the plan of the Yangtze River Delta Eco-Green Integrated Development Demonstration Zone (referred to as Demonstration Zone). The Demonstration Zone includes Qingpu District in Shanghai, Wujiang District in Suzhou City, Jiangsu Province, and some areas in Jiashan County, Jiaxing City, Zhejiang Province (Figure 1), which covers an area of about 2413 square kilometers [1]. At the same time, five towns in three districts and counties were selected as the first start-up areas, with an area of about 660 square kilometers [2]. Among them, Shanghai involves Jinze town and Zhujiajiao town, in Qingpu District.
The strategic aim of the Demonstration Zone was to build a new benchmark for transforming ecological advantages, a new highland for green innovation and development, and a new model for harmonious living between man and nature; these are all models of sustainable development. The goal was to become a benchmark for demonstrating and leading a higher quality integrated development of the Yangtze River Delta region. Therefore, the improvement of ambient air quality was bound to be an important part of green development in the Demonstration Zone. At present, there has been little relevant research focusing on air pollution in the Demonstration Zone [3]. In 2021, the World Health Organization revised and released the global Air Quality Guidelines (AQG 2021) to further reduce the global public health risks. AQG 2021 tightened the long-term exposure index of air pollutants (the annual average target value); for example, the annual average target value of PM2.5 decreased from 10 μg/m3 to 5 μg/m3 [4]. Therefore, more accurate environmental management measures are needed to further improve the ambient air quality in the Demonstration Zone.
In recent years, mobile air quality sensors have been widely used in the field of atmospheric environmental monitoring and pollution traceability since they are a measurement tool with low costs, high mobility and flexibility, and various application scenarios [5,6,7,8,9,10,11,12,13]. Compared with the fixed monitoring method, which has some shortcomings, such as blind spots in monitoring and the inability to accurately locate pollution sources, the use of a mobile air quality sensor improves the accuracy and coverage of environmental monitoring. A previous investigation, conducted in Spain, concluded that air quality levels reflected by the fixed stations are neither representative nor reliable due to the failure to identify where most of the population breathes heavily polluted air; it also highlighted the importance of mobile monitoring as a complementary technology [6]. The platforms of mobile air quality sensors mainly include taxis, bicycles, wearable devices, and drones, etc. [14,15,16,17,18,19].
In the current study, we selected two typical areas in Jinze town and Zhujiajiao town in Qingpu District (Figure 1), Shanghai, as study areas to conduct mobile monitoring of air pollutants from July to September 2022. The concentrations and spatial distribution of PM2.5, PM10, SO2, NO2, CO, and O3 were obtained. The temporal variations of pollutants were discussed and emission sources were identified. The data can provide a reference for the delicate management of ambient air quality in the Demonstration Zone.

2. Experiments and Methods

2.1. Description of Two Typical Study Areas in the Demonstration Zone

Qingxi Country Park (Country Park) and Zhujiajiao Ancient Town were selected as the representative areas of Jinze Town and Zhujiajiao Town (Figure S1), respectively, to carry out mobile monitoring. The Country Park’s main feature is wetlands. It should be noted that there is a village, Lianhu Village, located in the core area of the park (Figure S1a). Therefore, farmland is also an important component part of the park. Zhujiajiao Ancient Town, with an area of 3.08 square kilometers (Figure S1b), is famous for ancient buildings (private houses, ancestral temples, ancient bridges, etc.), and has become a unique tourist attraction in Shanghai.

2.2. Instrument

We used a portable air quality sensor network monitor (XHAQSN-806M, SailHero Inc., Shijiazhuang, China) to simultaneously measure PM10, PM2.5, SO2, NO2, O3, CO, temperature, and humidity in the atmospheric environment, with a time resolution of 5 s. The monitor was equipped with a built-in lithium battery and pumping sampling. The collected data were transmitted to the data center through the 4G/GPRS network, and the real-time measurement data can be viewed through the webpage or downloaded. Through GPS positioning, the data with the geographical location and the time of acquisition was obtained. The detailed technical parameters of the instrument can be referred to on the website: https://www.hbzhan.com/st124673/product_19446974.html (accessed on 19 December 2022). The portable monitor has been used in some Chinese local governments responsible for environmental monitoring and control. For example, in a public government bid-winning announcement. https://www.tianyancha.com/bid/4bf593d1cc504ac889ef50b3599a07a7 (accessed on 19 December 2022)), the purchase price of this instrument is 70,000 RMB, equivalent to more than 10,000 USD.
The instrument was fixed in a customized backpack and the air inlet was not sheltered when sampling. The experimenters carried backpacks and finished mobile observation experiments via an electric sightseeing bus, cycling or walking. Pictures of the monitor and photos taken during mobile observations can be seen in Figure S2.

2.3. Data Quality Control

Firstly, the calibration experiment of the portable instrument was carried out before using it. We placed the portable monitor at an automatic ambient air quality monitoring station for comparison. The automatic ambient air quality monitoring station (121.41° E, 31.17° N) was located on the rooftop of a six-floor building in Shanghai Normal University, and it released real-time hourly concentrations of PM10, PM2.5, SO2, NO2, O3 and CO. Data obtained from the portable instrument were calibrated. Figure 2 shows the comparison of data between the automatic ambient air quality monitoring station (Station) and the portable sensor monitor (Sensor). PM10, PM2.5, SO2, NO2, O3 and CO were measured by the portable sensor monitor and show good correlation with that by the automatic ambient air quality monitoring station, with a correlation coefficient (R2) of 0.87~0.99; this indicated that the portable sensor monitor could capture a variation trend in the pollutants.

2.4. Scheme of Mobile Observation

Statistics of the volume of visitors in the Ancient Town are depicted in Figure S3. The results show that the passenger flow of the ancient town significantly increased on weekends when compared with weekdays. Thus, mobile observations were conducted on weekends and weekdays from July to September 2022. One-day mobile observations cover different times of the day, which mainly include 08:00–10:00, 12:00–14:00, 16:00–18:00, and 20:00–22:00, to analyze the daily variation in pollutants. The monitoring was completed a total of 41 times, which is summarized in Table S1.

2.5. Single Aerosol Particle Analysis by Scanning Electron Microscopy (SEM)

Individual particle samples were collected on copper grids coated with carbon film (carbon type-B, 300-mesh copper; Tianld Co.,Beijing, China) by a cascade impactor (DKL-2, Genstar Electronic Technology, Qingdao, China) with a 0.5 mm diameter jet nozzle, at a flow rate of 1.0 L min−1. The sampling duration was approximately several minutes, depending on PM2.5 concentration. Individual particle samples were analyzed by SEM (Hitachi S-4800, Tokyo, Japan) to obtain the surface morphology and size of the aerosol particles. Detailed information about single aerosol particle collection and analysis can be found elsewhere [20].

3. Results

3.1. Air Quality in Qingpu District of the Demonstration Zone

Daily air quality data in Qingpu District from 2019–2021 were downloaded from the Shanghai Municipal Bureau of Ecological Environment https://sthj.sh.gov.cn/index.html (accessed on 1 March 2022). The air quality index (AQI) is divided into six grades according to 0–50, 51–100, 101–150, 151–200, 201–300 and 301–500, which are Grade I (Good), Grade II (Moderate), Grade III (Unhealthy for Sensitive Groups), Grade IV (Unhealthy), Grade V (Very Unhealthy) and Grade VI (Hazardous), respectively. The AQI shows that Good and Moderate accounted for 27.0% and 57.1% in Qingpu District from 2019–2021, respectively. The AQI shows that Unhealthy for Sensitive Groups and Unhealthy accounted for 13.6% and 2.2%, respectively. Only one day was classified as Very Unhealthy during the three years, and no Hazardous day was identified.
Table 1 lists concentrations of pollutants in Qingpu District of the Demonstration Zone from 2019 to 2021. China’s National ambient air quality standard (GB 3095-2012, hereafter NAAQS-2012), which, in line with interim target 1 of WHO global air quality guidelines (AQG 2021), specifies the annual concentration limits for the six key air pollutants for Grade II function areas (Table 1); these include residential areas, commercial, transportation and residential mixed areas, cultural areas, and industrial areas, as well as rural areas. Based on the new evidence regarding death caused by long-term exposure to air pollution, AQG 2021 recommended more rigorous guidelines for the six key air pollutants, as seen in Table 1. The annual average concentrations of PM2.5, PM10, NO2 and SO2 in Qingpu District were 35 μg/m3, 47 μg/m3, 36 μg/m3, and 6 μg/m3, respectively, which meets the NAAQS-2012. The rate of exceeding NAAQS-2012, based on a 24-h concentration, was 5.2% for PM2.5, 0.8% for PM10, 1.6% for NO2 and 0% for SO2. There were 108 days, 9.9% of the total 1096 days, when O3-8h (daily maximum 8-h mean) exceeded the NAAQS-2012 (160 μg/m3) in Qingpu District. The average of the daily maximum 8-h mean of O3 concentration was 100 μg/m3 from 2019 to 2021, which exceeded O3 guidelines of AQG 2021. The daily average of SO2 ranged from 2 to 17 μg/m3 in Qingpu District during the three years, which meets the AQG 2021 (20 μg/m3 for daily SO2). The daily average of CO ranged from 0.3 to 1.4 mg/m3, with a mean value of 0.578 mg/m3 in Qingpu District during the three years, which meets the AQG 2021 (4 mg/m3 for daily CO). Thus, the average level of SO2 and CO in Qingpu District has negligible adverse effects on human health.
In summary, air quality in the Demonstration Zone reached the National Ambient Air Quality Standard. Health risks from air pollution exposure in the Demonstration Zone were attributed to particulate matter, as well as NO2 and O3.

3.2. Spatial Distribution of Pollutants in Demonstration Zone

The spatial variation in pollutants in the Ancient Town and the Country Park is depicted in Figure 3. To eliminate the interference of background concentrations of pollutants, we selected the observations with similar air quality to compare the pollutant concentrations in the Country Park and the Ancient Town. For example, daily concentrations of PM2.5 in Qingpu District on 17 August and 18 August were very similar (21 μg/m3 and 22 μg/m3, respectively). PM2.5 in the country park mostly ranged from 15 to 20 μg/m3, and that in the Ancient Town ranged from 20 to 50 μg/m3. PM10 in the two areas were comparable. It is inferred that soil dust caused by agricultural activities is the main source of PM10 in the country park and dust resuspension is the major source of PM10 in the Ancient Town. SO2 level in the Ancient Town was obviously higher than that in the Country Park. Similarly, NO2, CO and O3 in the Ancient Town were higher than that in the Country Park. Affected by the high intensity of human activities, the concentration of the six key pollutants in the Ancient Town is usually higher than that in the Country Park. To investigate the impact of residential emission on air pollution in the country park, the mobile monitoring route extends to the inside of Lianhu Village, as shown in Figure 4. Primary pollutants, including SO2, NO2 and CO, around Lianhu Village were higher than that in other areas of the Country Park. SO2, NO2 and CO were probably emitted from the burning of household solid fuels, which includes coal and firewood.

3.3. Temporal Variation in Pollutants in Demonstration Zone

3.3.1. Comparison of Pollutants in Weekdays and Weekends

The response of pollutants is closely related to human activities. Comparison of pollutants in weekdays and weekends at the Ancient Town and the Country Park are depicted in Figure 5. Weekdays include Wednesday and Thursday (12:00–14:00 and 16:00–18:00, 17–18 August) and weekends include Saturday and Sunday (12:00–14:00 and 16:00–18:00, 20–21 August). The air quality in Qingpu District during the studied weekdays was comparable with that during the studied weekends. For example, the daily concentrations of PM2.5 in Qingpu District on 17 August, 18 August, 20 August, and 21 August were 21 μg/m3, 22 μg/m3, 24 μg/m3 and 26 μg/m3 Data source: https://sthj.sh.gov.cn/ (accessed on 1 November 2022). The concentrations of PM2.5, PM10, SO2 and CO in the ancient town during weekends were higher than that during weekdays (Figure 5a). NO2 levels in the Ancient Town during weekdays and weekends were similar. The results were different from the variation in pollutants in typical urban areas during weekends and weekdays. Lower concentrations of pollutants, especially NOx, were usually observed during weekends when compared with that of weekdays [21,22,23]. The higher concentration of pollutants in the Ancient Town during weekdays was closely related to the increased volume of visitors. Concentrations of pollutants in the country park presented no consistent variation between weekdays and weekends (Figure 5b).

3.3.2. Daily Variation in Pollutants

Daily variations in the typical pollutants in the Ancient Town and the Country Park are depicted in Figure 6. Data used to calculate daily variations in pollutants in the Country Park include monitoring observations labeled Number 14–21 and Number 30–37, and that include Number 10–13, 22–29, 38–41 for the Ancient Town, as seen in Table S1. NO2 in the Ancient Town and the country park both presented relatively higher concentrations during 08:00–10:00. Our previous study revealed that NO2 increased sharply in the morning rush hour, which was conducted in Qingpu District, Shanghai [24]. Thus, higher NO2 concentration during 08:00–10:00 in the two areas was probably affected by the transport of pollutants from vehicle emission during the morning rush hour. O3 in the two areas presented higher concentrations in the daytime because O3 is the product of photochemical reaction. It should be noted that O3 in the studied areas remained at a high level (>100 μg/m3) at night. Similarly, high levels of O3 at night were detected at some automatic air quality monitoring stations in the suburbs of Shanghai and the Demonstration Zone (Figure S4). For example, hourly O3 varied from 129 μg/m3 to 220 μg/m3 during 20:00–22:00 (Figure S4). The study period was characterized by continuous extremely hot weather, which promoted daytime O3 formation. There were 22 days and 16 days in which the daily maximum temperature exceeded 36 °C and 38 °C in August 2022, respectively (Data source: Shanghai Meteorological Bureau, http://sh.cma.gov.cn/sh/tqyb/gwqk/ (accessed on 1 November 2022). High nighttime surface O3 concentrations were also reported in previous studies in China and Malaysia, which is caused by the turbulent transport of O3 reserved over the stable boundary layer and lower nitrogen oxide concentrations [25,26].
PM10 in the country park presented relatively higher concentrations during 08:00–10:00 and 16:00–18:00, which was probably associated with agricultural production activities. On the contrary, PM10 in the country park displayed the lowest concentration during 20:00–22:00. A lower concentration of PM10 was observed during 12:00–14:00 due to the highest temperature at noon, and consequently less anthropogenic activities. Concentrations of SO2 in the country park were relatively high during the day and sharply decreased during 20:00–22:00, which was likely related to emissions from residential activities, such as cooking powered by coal or wood combustion, or agricultural activities in the daytime.

3.4. Identification of Sources in Case Studies

With the rapid response of mobile monitoring observation, emission sources were identified in the field from case studies, as illustrated in Figure 7. The time series of typical pollutants in case studies are displayed in Figure 8. In the country park, agricultural irrigation, which is powered by diesel combustion, was identified as one of emission sources in the country park (Figure 7). The pollution case was characterized by an extremely high concentration of SO2 and CO, with the real-time maximum concentration of SO2 and CO up to 1530 μg/m3 and 20 mg/m3, respectively. A similar pollution case, caused by agricultural irrigation, was also observed during 14:00–16:00 on the 18 August in the country park (Figure 3). Yasar et al. conducted a comparison of pollutant emissions from different fuel and engine types, and found that SO2 emissions were highest for diesel [27]. Zhang et al. investigated emission factors for gaseous and particulate pollutants from diesel engine vessels, and found that the highest CO was observed in idling mode [28].
In the Ancient Town, specialty food attracts a large number of tourists. The popular street food is usually made by open-air cooking, which uses coal as fuel. For example, variation trends in PM2.5, SO2 and CO agreed well with each other in this case (Figure 8b); these were all probably emitted by coal combustion. Concentrations of PM2.5 and SO2 were up to 118 μg/m3 and 58 μg/m3, respectively. Restaurants cooking may also be a non-negligible source of atmospheric pollutants due to the high density of restaurants in and around the Ancient Town. PM2.5, ultrafine particles and organic matter, and VOCs generated from cooking, were widely reported [29,30,31]. Song et al. investigated the impacts of diesel bus traffic and restaurant cooking on spatial variations in urban air pollution, and highlighted the importance of restaurant cooking at small scales [32]. In addition, frequent renovations caused by the closing and opening of shops and stores is a significant source of PM10 in the Ancient Town. Environmentally friendly irrigation methods and cleaner household fuels for cooking were suggested to further improve air quality in the study areas.

3.5. Morphology of Individual Particles

Surface morphology and sizes of individual aerosol particles from SEM analysis in the Ancient Town and the Country Park are depicted in Figure 9. Li et al. reviewed the morphology, mixing state and source of various single particles in East Asia; this was referred to in this study, in order to identify aerosol types due to a lack of EDX analysis [33]. Abundant soot particles, usually emitted from the incomplete combustion of fossil fuels and biomass, were identified in both areas (Figure 9c,d,g–j). Soot particles, also referred to as black carbon (BC), are chain-like aggregates of carbon-bearing spheres with typical diameters from 10 to 100 nm [34]. Fresh soot particles were branched (Figure 9c,j) and will become more compact (Figure 9d,g) during transport and aging [35,36]. Fly ash (Figure 9b), mainly containing Al and Si, is typically produced by coal combustion for cooking, heating, industrial activities, and power plants [33]. Figure 9e shows one spherical primary organic particle. The sulfur-rich particles, which are secondarily formed from SO2, have a special morphology in the electron microscope, and usually appear as foam (Figure 9l). Figure 9f,k were inferred to be aged mineral (CaSO4), which could be produced from calcite and dolomite reacting with acidic gases (e.g., SO2) [33]. In summary, the morphology analysis of the aerosol particles indicated that air pollution in the two studied areas was affected by local emissions from small scale activities and regional transport.

4. Discussion

Our work evaluated the advantages and disadvantages of the mobile sensor monitor utilized. On one hand, the portable monitor could provide more reliable O3 concentration data, compared with the other five key pollutants. A slope of 0.99 was obtained between O3, measured by the portable sensor monitor and the monitoring station, with R2 of 0.96 (Figure 2). On the other hand, the portable instrument was easily disturbed by abundant water vapor when measuring particulate matter. A higher concentration of particles was usually obtained at a high relative humidity. For example, the concentration of PM2.5 measured by the instrument increased significantly when encountering a sprinkler and sudden rain during the mobile observation. Data correction is required, as a previous study of the review emphasized [9]. In addition, the portable monitor was designed for vehicle-borne navigation. In summary, our evaluation of the instrument provides a reference for other researchers or government departments considering using this instrument.
The most prevalent application of the mobile monitoring of air pollution includes (1) assessment of personal exposure, (2) the supplementation of existing air pollution monitor networks, and (3) citizen science or education [37]. In the Demonstration Zone, there is only one state-controlled monitoring station that has released real-time hourly air quality monitoring data since 2021. Thus, the application of the mobile monitoring in this work tried to provide supplementation to existing air pollution monitor networks. The contribution of this study lies in the identification of pollution sources in the Demonstration Zone through mobile observation, such as agricultural irrigation activities, open-air cooking, and cooking by solid fuels.
There are some limitations in this study. Due to the serious influence of the epidemic (COVID-19) in the first half of 2022 in Shanghai and the requirement of project completion, we only conducted mobile observation in July, August, and September, which is only representative of the warm season. There is a lack of data on the cold season, in which heavy pollution often occurs. It is suggested that further investigation is conducted in the Demonstration Zone based on vehicle-mounted navigation in the cold season.

5. Conclusions

In the current work, we conducted mobile observation of atmospheric pollutants in a country park and an ancient town, as typical areas in the Demonstration Zone. Results from the spatial distribution, comparison between weekdays and weekends, and the daily temporal variation in pollutants indicated that the concentration change in pollutants is closely related to human activities at small scales. Morphology analysis detected aerosol particles with small size and complicated compositions, including fresh soot, aged soot, organics, fly ash and S-rich particles, which implied the complex origins of pollutants in the studied areas. Agricultural irrigation, household solid fuels, open-air cooking, coal combustion for cooking and the frequent redecoration of stores were emission sources in the studied areas. Environmentally friendly agricultural irrigation methods and cleaner cooking fuels were suggested to further improve air quality and reduce health risks in the Demonstration Zone. The study can support decision making for the delicate management of ambient air quality in the Demonstration Zone.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su15010205/s1, Figure S1: Map of (a) Qingxi Country Park (Country Park) and (b) Zhujiajiao Ancient Town (Ancient Town).; Figure S2: Pictures of the monitor and photos taken during mobile observations.; Figure S3: Statistics of passenger flow of the Ancient Town from 28 June to 6 August, 2022.; Table S1: Summary of mobile observation in the study.; Figure S4: Typical case of abnormal high level of O3 observed at night in suburban sites, Shanghai and in the Demonstration zone during August, 2022.

Author Contributions

Software, Q.C.; validation, J.Y.; formal analysis, J.Y.; investigation, X.F., Q.C., Y.Y., Y.X. and F.Z.; resources, L.Q.; data curation, X.F., Q.C., Y.Y., Y.X. and F.Z.; writing—original draft preparation, X.F. and L.Y.; writing—review and editing, L.Y., L.Q. and W.L.; visualization, Q.C. and J.Y.; supervision, L.Y.; project administration, X.F. and L.Y.; funding acquisition, L.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Shanghai Undergraduate Training Program on Innovation and Entrepreneurship (SUTPI) (No. 202210270027), National Natural Science Foundation of China (No. 42005089), State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex (No. 2021080539), and Opening Project of Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3) (No. FDLAP20007).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data available on reasonable request from the corresponding author.

Acknowledgments

Great appreciation to Peng Fu, SailHero Inc., China, for providing the portable air quality sensor monitor.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. He, T.; Tian, N.; Zhou, R.; Ma, Q.; Zhang, J.; Gao, R. Dynamic assessment of eco-environmental quality in Yangtze River Delta integration demonstration area based on GEE and RSEI. Chin. J. Ecol. 2022, 1–13. Available online: https://kns.cnki.net/kcms/detail/21.1148.Q.20220619.2206.002.html (accessed on 1 November 2022). (In Chinese).
  2. Liu, H.; Guo, W.; Peng, X.; Yang, J. Research on the green governance model of the Yangtze River Delta Eco-Green Integrated Development Demonstration Zone. Shanghai Urban Manag. 2022, 05, 2–11. (In Chinese) [Google Scholar]
  3. Xu, D.; Lin, W.; Shi, R.; Gao, J.; Li, L. Spatial-temporal evolution patterns of PM2.5 exposure risk in the Yangtze River Delta Ecological Greening Development Demonstration Area during 2001—2020. Acta Sci. Circumstantiae 2022, 42, 36–52. (In Chinese) [Google Scholar]
  4. World Health Organization (WHO). WHO Global Air Quality Guidelines: Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide; World Health Organization: Geneva, Switzerland, 2021. [Google Scholar]
  5. Bertero, C.; Leon, J.F.; Tredan, G.; Roy, M.; Armengaud, A. Urban-Scale NO2 prediction with sensors aboard bicycles: A comparison of statistical methods using synthetic observations. Atmosphere 2020, 11, 1014. [Google Scholar] [CrossRef]
  6. Lorenzo-Saez, E.; Oliver-Villanueva, J.V.; Lemus-Zuniga, L.G.; Coll-Aliaga, E.; Castillo, C.P.; Lavalle, C. Assessment of an air quality surveillance network through passive pollution measurement with mobile sensors. Environ. Res. Lett. 2021, 16, 054072. [Google Scholar] [CrossRef]
  7. Yeom, K. Development of urban air monitoring with high spatial resolution using mobile vehicle sensors. Environ. Monit. Assess. 2021, 193, 375. [Google Scholar] [CrossRef]
  8. Sun, Y.X.; Brimblecombe, P.; Wei, P.; Duan, Y.; Pan, J.; Liu, Q.Z.; Fu, Q.Y.; Peng, Z.G.; Xu, S.H.; Wang, Y.; et al. High resolution on-road air pollution using a large taxi-based mobile sensor network. Sensors 2022, 22, 6005. [Google Scholar] [CrossRef]
  9. Baron, R.; Saffell, J. Amperometric gas sensors as a low cost emerging technology platform for air quality monitoring applications: A review. ACS Sens. 2017, 2, 1553–1566. [Google Scholar] [CrossRef]
  10. Zhang, S.; Liu, H.; Tang, N.; Ali, N.; Yu, J.; Ding, B. Highly Efficient, Transparent, and Multifunctional Air Filters Using Self-Assembled 2D Nanoarchitectured Fibrous Networks. ACS Nano 2019, 13, 13501–13512. [Google Scholar] [CrossRef]
  11. Zhang, S.; Liu, H.; Tang, N.; Zhou, S.; Yu, J.; Ding, B. Spider-Web-Inspired PM0.3 Filters Based on Self-Sustained Electrostatic Nanostructured Networks. Adv. Mater. 2020, 32, 2002361. [Google Scholar] [CrossRef]
  12. Chen, Y.; Gu, P.; Schulte, N.; Zhou, X.; Mara, S.; Croes, B.E.; Herner, J.D.; Vijayan, A. A new mobile monitoring approach to characterize community-scale air pollution patterns and identify local high pollution zones. Atmos. Environ. 2022, 272, 118936. [Google Scholar] [CrossRef]
  13. Batur, I.; Markolf, S.A.; Chester, M.V.; Middel, A.; Hondula, D.; Vanos, J. Street-level heat and air pollution exposure informed by mobile sensing. Transp. Res. D Transp. Environ. 2022, 113, 103535. [Google Scholar] [CrossRef]
  14. Elen, B.; Peters, J.; Poppel, M.V.; Bleux, N.; Theunis, J.; Reggente, M.; Standaert, A. The aeroflex: A bicycle for mobile air quality measurements. Sensors 2013, 13, 221–240. [Google Scholar] [CrossRef] [PubMed]
  15. Velasco, A.; Ferrero, R.; Gandino, F.; Montrucchio, B.; Rebaudengo, M. A mobile and low-cost system for environmental monitoring: A case study. Sensors 2016, 16, 710. [Google Scholar] [CrossRef] [PubMed]
  16. Burgués, J.; Marco, S. Environmental chemical sensing using small drones: A review. Sci. Total Environ. 2020, 748, 141172. [Google Scholar] [CrossRef]
  17. Gressent, A.; Malherbe, L.; Colette, A.; Rollin, H.; Scimia, R. Data fusion for air quality mapping using low-cost sensor observations: Feasibility and added-value. Environ. Int. 2020, 143, 105965. [Google Scholar] [CrossRef]
  18. Santana, P.; Almeida, A.; Mariano, P.; Correia, C.; Martins, V.; Almeida, S.M. Air quality mapping and visualisation: An affordable solution based on a vehicle-mounted sensor network. J. Clean. Prod. 2021, 315, 128194. [Google Scholar] [CrossRef]
  19. Palomeque-Mangut, S.; Meléndez, F.; Gómez-Suárez, J.; Frutos-Puerto, S.; Arroyo, P.; Pinilla-Gil, E.; Lozano, J. Wearable system for outdoor air quality monitoring in a WSN with cloud computing: Design, validation and deployment. Chemosphere 2022, 307, 135948. [Google Scholar] [CrossRef]
  20. Li, W.; Liu, L.; Xu, L.; Zhang, J.; Yuan, Q.; Ding, X.; Hu, W.; Fu, P.; Zhang, D. Overview of primary biological aerosol particles from a Chinese boreal forest: Insight into morphology, size, and mixing state at microscopic scale. Sci. Total Environ. 2020, 719, 137520. [Google Scholar] [CrossRef]
  21. Fang, C.; Xue, K.; Li, J.; Wang, J. Characteristics and weekend effect of air pollution in eastern Jilin Province. Atmosphere 2022, 13, 681. [Google Scholar] [CrossRef]
  22. Elansky, N.F.; Shilkin, A.V.; Ponomarev, N.A.; Semutnikova, E.G.; Zakharova, P.V. Weekly patterns and weekend effects of air pollution in the Moscow megacity. Atmos. Environ. 2020, 224, 117303. [Google Scholar] [CrossRef]
  23. Demetillo, M.A.G.; Harkins, C.; McDonald, B.C.; Chodrow, P.S.; Sun, K.; Pusede, S.E. Space-based observational constraints on NO2 air pollution inequality from diesel traffic in major US cities. Geophys. Res. Lett. 2021, 48, e2021G–e94333G. [Google Scholar] [CrossRef]
  24. Yao, L.; Huo, J.; Wang, D.; Fu, Q.; Sun, W.; Li, Q.; Chen, J. Online measurement of carbonaceous aerosols in suburban Shanghai during winter over a three-year period: Temporal variations, meteorological effects, and sources. Atmos. Environ. 2020, 226, 117408. [Google Scholar] [CrossRef]
  25. Hu, J.; Hu, X.; Gao, L.; Cai, C.; Zhao, T.; Zhang, X. Impacts of nocturnal cloud top radiative cooling on surface O3 in Sichuan Basin, Southwestern China. Earth Space Sci. 2021, 8, e1541E–e2020E. [Google Scholar] [CrossRef]
  26. Awang, N.R.; Ramli, N.A.; Yahaya, A.S.; Elbayoumi, M. High nighttime ground-level ozone concentrations in Kemaman: NO and NO2 concentrations attributions. Aerosol Air Qual. Res. 2015, 15, 1357–1366. [Google Scholar] [CrossRef]
  27. Yasar, A.; Haider, R.; Tabinda, A.B.; Kausar, F.; Khan, M. A comparison of engine emissions from heavy, medium, and light vehicles for CNG, diesel, and gasoline fuels. Pol. J. Environ. Stud. 2013, 22, 1277–1281. [Google Scholar]
  28. Zhang, F.; Chen, Y.; Tian, C.; Lou, D.; Li, J.; Zhang, G.; Matthias, V. Emission factors for gaseous and particulate pollutants from offshore diesel engine vessels in China. Atmos. Chem. Phys. 2016, 16, 6319–6334. [Google Scholar] [CrossRef] [Green Version]
  29. Wang, H.; Xiang, Z.; Wang, L.; Jing, S.; Lou, S.; Tao, S.; Liu, J.; Yu, M.; Li, L.; Lin, L.; et al. Emissions of volatile organic compounds (VOCs) from cooking and their speciation: A case study for Shanghai with implications for China. Sci. Total Environ. 2018, 621, 1300–1309. [Google Scholar] [CrossRef] [Green Version]
  30. Pei, B.; Cui, H.; Liu, H.; Yan, N. Chemical characteristics of fine particulate matter emitted from commercial cooking. Front. Env. Sci. Eng. 2016, 10, 559–568. [Google Scholar] [CrossRef]
  31. Chen, C.; Zhao, Y.; Zhao, B. Emission rates of multiple air pollutants generated from Chinese residential cooking. Environ. Sci. Technol. 2018, 52, 1081–1087. [Google Scholar] [CrossRef]
  32. Song, R.; Presto, A.A.; Saha, P.; Zimmerman, N.; Ellis, A.; Subramanian, R. Spatial variations in urban air pollution: Impacts of diesel bus traffic and restaurant cooking at small scales. Air Qual. Atmos. Health 2021, 14, 2059–2072. [Google Scholar] [CrossRef]
  33. Li, W.; Shao, L.; Zhang, D.; Ro, C.; Hu, M.; Bi, X.; Geng, H.; Matsuki, A.; Niu, H.; Chen, J. A review of single aerosol particle studies in the atmosphere of East Asia: Morphology, mixing state, source, and heterogeneous reactions. J. Clean. Prod. 2016, 112, 1330–1349. [Google Scholar] [CrossRef]
  34. Buseck, P.R.; Adachi, K.; Gelencsér, A.; Tompa, É.; Pósfai, M. Ns-soot: A material-based term for strongly light-absorbing carbonaceous particles. Aerosol Sci. Technol. 2014, 48, 777–788. [Google Scholar] [CrossRef] [Green Version]
  35. Zhang, R.; Khalizov, A.F.; Pagels, J.; Zhang, D.; Xue, H.; McMurry, P.H. Variability in morphology, hygroscopicity, and optical properties of soot aerosols during atmospheric processing. Proc. Natl. Acad. Sci. USA 2008, 105, 10291–10296. [Google Scholar] [CrossRef] [PubMed]
  36. Wang, Y.; Liu, F.; He, C.; Bi, L.; Cheng, T.; Wang, Z.; Zhang, H.; Zhang, X.; Shi, Z.; Li, W. Fractal dimensions and mixing structures of soot particles during atmospheric processing. Environ. Sci. Technol. Lett. 2017, 4, 487–493. [Google Scholar] [CrossRef]
  37. McKercher, G.R.; Salmond, J.A.; Vanos, J.K. Characteristics and applications of small, portable gaseous air pollution monitors. Environ. Pollut. 2017, 223, 102–110. [Google Scholar] [CrossRef]
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Figure 1. Location of the Yangtze River Delta Eco-Green Integrated Development Demonstration Zone.
Figure 1. Location of the Yangtze River Delta Eco-Green Integrated Development Demonstration Zone.
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Figure 2. Comparison of pollutants measured by the portable monitor and automatic ambient air quality monitoring station.
Figure 2. Comparison of pollutants measured by the portable monitor and automatic ambient air quality monitoring station.
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Figure 3. Spatial distribution of pollutants during 14:00–16:00 in the Ancient Town (17 August, observation numbered in Table S1) and the Country Park (18 August, observation numbered in Table S1).
Figure 3. Spatial distribution of pollutants during 14:00–16:00 in the Ancient Town (17 August, observation numbered in Table S1) and the Country Park (18 August, observation numbered in Table S1).
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Figure 4. Spatial variation in pollutants in the Country Park (12:00–14:00, 24 July, observation numbered 7 in Table S1).
Figure 4. Spatial variation in pollutants in the Country Park (12:00–14:00, 24 July, observation numbered 7 in Table S1).
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Figure 5. Comparison of pollutants in weekdays and weekends at (a) the Ancient Town and (b) the Country Park. Weekdays include Wednesday to Friday (observation numbered 10, 13, 16 and 17 in Table S1) and weekends include Saturday and Sunday (observation numbered 20–22 and 25 in Table S1).
Figure 5. Comparison of pollutants in weekdays and weekends at (a) the Ancient Town and (b) the Country Park. Weekdays include Wednesday to Friday (observation numbered 10, 13, 16 and 17 in Table S1) and weekends include Saturday and Sunday (observation numbered 20–22 and 25 in Table S1).
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Figure 6. Daily variation in typical pollutants in the Ancient Town (a,b) and the Country Park (c–f).
Figure 6. Daily variation in typical pollutants in the Ancient Town (a,b) and the Country Park (c–f).
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Figure 7. Case study of high concentration of typical pollutants in the Country Park (Observation numbered 8 in Table S1) and the Ancient Town (Observation numbered 25 in Table S1).
Figure 7. Case study of high concentration of typical pollutants in the Country Park (Observation numbered 8 in Table S1) and the Ancient Town (Observation numbered 25 in Table S1).
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Figure 8. Time series of typical pollutants in (a) the Country Park and (b) the Ancient Town in case studies.
Figure 8. Time series of typical pollutants in (a) the Country Park and (b) the Ancient Town in case studies.
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Figure 9. Surface morphology and sizes of individual aerosol particles from SEM analysis in the Ancient Town (a–f) and the Country Park (g–l).
Figure 9. Surface morphology and sizes of individual aerosol particles from SEM analysis in the Ancient Town (a–f) and the Country Park (g–l).
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Table
Table 1. Summary of air quality in Qingpu District of the Demonstration Zone from 2019 to 2021.
Table 1. Summary of air quality in Qingpu District of the Demonstration Zone from 2019 to 2021.
PollutantsTypeNAAQS-2012AQG 2021Qingpu DistrictExceeding NAAQS-24 h
PM2.5
(μg/m3)		Annual35535-
24-h75154–1625.2%
PM10
(μg/m3)		Annual702047-
24-h150506–2860.8%
SO2
(μg/m3)		Annual60-6-
24-h150202–170.0%
NO2
(μg/m3)		Annual401036-
24-h80254–1081.6%
CO
(mg/m3)		Annual--0.578-
24-h440.3–1.40.0%
O3
(μg/m3)		Peak season -60--
8-h a16010014–2619.90%
-: Not applicable. a: Daily maximum 8-h mean value.
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Fu, X.; Cai, Q.; Yang, Y.; Xu, Y.; Zhao, F.; Yang, J.; Qiao, L.; Yao, L.; Li, W. Application of Mobile Monitoring to Study Characteristics of Air Pollution in Typical Areas of the Yangtze River Delta Eco-Green Integration Demonstration Zone, China. Sustainability 2023, 15, 205. https://doi.org/10.3390/su15010205

AMA Style

Fu X, Cai Q, Yang Y, Xu Y, Zhao F, Yang J, Qiao L, Yao L, Li W. Application of Mobile Monitoring to Study Characteristics of Air Pollution in Typical Areas of the Yangtze River Delta Eco-Green Integration Demonstration Zone, China. Sustainability. 2023; 15(1):205. https://doi.org/10.3390/su15010205

Chicago/Turabian Style

Fu, Xinran, Qixin Cai, Yitao Yang, Yu Xu, Fanghong Zhao, Jie Yang, Liping Qiao, Lan Yao, and Weiyue Li. 2023. "Application of Mobile Monitoring to Study Characteristics of Air Pollution in Typical Areas of the Yangtze River Delta Eco-Green Integration Demonstration Zone, China" Sustainability 15, no. 1: 205. https://doi.org/10.3390/su15010205

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