代写GEOG0016 The Practice of Geography 2023-24代写数据结构语言程序

YEAR 2023-24

MODULE CODE:	GEOG0016
MODULE NAME:	The Practice of Geography
COURSE PAPER TITLE:	Integrating Satellite and Ground Data for Spatial and Temporal Air Pollution in Guangdong-Hong Kong-Macao Greater Bay Area
WORD COUNT:	2415

Integrating Satellite and Ground Data for Spatial and Temporal Air

Pollution in Guangdong-Hong Kong-Macao Greater Bay Area

1 Aims and Objectives

The importance of clean air for health and the environment is clear. To study air quality, satellite and ground data are used. Satellite data uses remote sensing to cover wide areas with many details overtime. Ground data gives accurate local readings. However, both data sources have their  limitations(Benavente  et  al.,  2021),  satellite  data  can  be  affected  by  atmospheric conditions and cloud cover, while ground-based monitoring data has limited spatial coverage. With rapid economic development and population growth, Guangdong-Hong Kong-Macao Greater Bay Area, is facing an increasingly serious air pollution problem, so integrating and analyzing   satellite   observations   and   ground-based  monitoring  data   is   essential  for  a comprehensive understanding of air quality changes in Shenzhen. The aim of this study is to combine satellite and ground monitoring data to conductspatio-temporal analysis of Shenzhen's air  quality.  Specifically,  we  will  focus  on  temporal  changes  in  PM2.5    and  O3    levels  in Guangdong-Hong Kong-Macao Greater Bay Area, and explore differences in how satellite and ground-based monitoring data represent changes in these air pollutants.

Research Questions:

1.   What are the differences between satellite data and ground data?

2.   What are the spatio-temporal variations of PM2.5, O3  in Guangdong-Hong Kong-Macao Greater Bay Area?

3.   What factors affect the variations of PM2.5, O3 in Guangdong-Hong Kong-Macao Greater Bay Area?

2 Context and Previous Work

The Guangdong-Hong Kong-Macao Greater Bay Area is a city cluster consisting of two sub- provincial cities of Guangzhou and Shenzhen in Guangdong Province, seven prefecture-level cities of Zhuhai, Foshan, Dongguan, Zhongshan, Jiangmen, Huizhou and Zhaoqing, and two special administrative regions (Hong Kong Special Administrative Region and Macao Special Administrative Region). Due to its superior geographical location (see Figure 1), Become an international city with a population of more than millions (Hui et al., 2020). As one of the regions with the fastest economic development in the world in the past 30 years, the problem of fog  and  haze has become  increasingly  serious,  and  the  environmental  and  human  health problems caused by it have attracted more and more attention (Liu et al., 2021). Air pollution is a difficult problem faced by most developing countries in the process of urbanization and industrialization. More and more attention has been paid to the investigation and study of atmospheric environment.

Over the past few decades, with rapid urbanization and industrialization, large amounts of Particulate matter (PM) have been emitted into the atmosphere, resulting in some severe haze episodes everywhere. PM2.5  (particulate matter with an aerodynamic diameter ≤ 2.5  μm) has been shown to have a significant impact on the climate system and human health (Changetal., n.d.; Chen et al., 2021) and aroused wide public concern. The Chinese government has taken a series  of  emission  reduction  measures  and  achieved   some   success,  but  the   average concentration of particulate matter is still higher than the World Health Organization's health standard, especially in the central and eastern plains and basins of China. At the sametime, the concentration of ozone (O3) continues to increase, and now ithas become one of the important atmospheric  pollutants  in  the  lower  urban  areas.  In  the  past  10  years,  the  global  ozone concentration has shown a rising trend (Wang et al., 2023). From 2010 to 2021, some scholars have found that O3 pollution occurs frequently all over China, especially in central and southern China, especially in coastal cities(Wang et al., 2023). O3  can increase the oxidation of the atmosphere, promote  the  oxidation  of  SO2,  NO2  and  VOCs  in  the  atmosphere  and  the conversion of gas particles into particulate matter, and then enhance the pollution of PM2.5  and other particulate matter. High concentrations of ground-based O3  also increase the frequency of urban photochemical smog (Quet al., 2023).

Although ground-based observation technology has the advantage of high accuracy for air pollutant  monitoring,  its  shortcomings  are  also  obvious.  Taking  PM2.5    observation  as  an example, up to now, there is relatively lack of observation means for PM2.5, poor systematic data,  sparse  distribution  of stations, and inability to obtain large-scale  spatial  distribution characteristics of PM2.5, while satellite remote sensing covers a wide range. The inversion information is more comprehensive, which can effectively make up for the shortcomings of ground monitoring stations in space. Therefore, the use of satellite remote sensing data to analyze  the  regional  distribution  and  change  of  PM2.5,  atmospheric  aerosol  and   other atmospheric components has gradually become an important technical means. Satellite remote sensing technology can provide distribution information of various atmospheric components including aerosol optical thickness (AOD or AOT) at regional and even global scales, and provide an effective means for understanding pollution distribution, sources and interregional transmission processes at different  scales  (Y. Zhang et al., 2021; Zhao et al., 2023).  The significant increase of aerosol content in the atmosphere not only poses a great threat to urban air quality, but also has a great impact on regional environment and climate (Gao et al., 2023). Studies have shown that there are interaction and transport problems of air pollution among cities (Q. Zhang et al., 2022), so the distribution characteristics of aerosols are closely related to the sources of pollutants. Among the parameters that characterize the physical properties of aerosols, AOD reflects the extinction effect of aerosol particles on solar radiation, which can be used to calculate the aerosol content and assess the degree of atmospheric pollution  (S. Zhang et al., 2020).

The research results  of the  above  scholars  have  important  reference  significance  for  the investigation of the current situation of air pollution in the Guangdong-Hong Kong-Macao Greater Bay Area and the in-depth understanding of the characteristics and causes of air pollution, but most of these studies are aimed at a short period of time or carry out long-term analysis  based  on  a  single  element  of  air  pollution.  However,  the  investigation  of  the distribution status of pollutants in different time scales and different Spaces, the long-term change rules and evolution characteristics are less discussed. Based on ground observation data and satellite remote sensing data, taking PM2.5  and O3  as examples, this paper analyzes the investigation and spatio-temporal evolution characteristics of air pollution in the Guangdong- Hong Kong-Macao Greater Bay Area based on ground observation and satellite monitoring data and the current situation of air pollution in the Guangdong-Hong Kong-Macao Greater Bay Area, focusing on the pollution characteristics presented in different time and space. Through comparison and correlation coefficient calculation, we can find out how satellite data and ground data help us better understand the changes of air quality overtime and space, so as to  provide  scientific  basis  for  the  system  construction  of  environmental   governance, environmental protection and emission reduction measures.

Figure 1 study area

3 Research Data and Method

3.1 Site monitoring data

Two  types  of  ground  data  were  used  for  the  analysis.  The  first  category  is  based  on environmental monitoring data obtained from hourly monitoring data of 56 observation stations in  the  Greater  Bay  Area  from  2015  to  2021  provided  by  the  Ministry  of  Ecology  and Environment, PRC (http://www.mee.gov.cn/), including PM2.5  and O3-8h air pollutant data. The  second  type  of surface  data  is  the  Daily  value  dataset  (V3.0)  of the  China  Surface International Exchange Climatological Data, which includes hourly meteorological data from four stations in Guangzhou, Shenzhen, Hong Kong and Macao during the period from 2015 to 2021.

Table 1 Concentration limits of conventional ambient air pollutants

Pollutant	Average Time	Mass concentration limits		Unit
		Primary limit	Secondary limit
PM2.5	24-hour average	35	75	μg/m3
	Annual average	15	35	μg/m3
O3	Daily maximum 8h average	100	160	μg/m3
NO2	24-hour average	80	80	μg/m3
	Annual average	40	40	μg/m3
SO2	24-hour average	50	150	μg/m3
	Annual average	20	60	μg/m3
PM10	24-hour average	50	150	μg/m3
	Annual average	40	70	μg/m3
CO	24-hour average	4	4	mg/m3

3.2 Satellite observation data

The annual average PM2.5  concentration data is based on the ground-based PM2.5  observation data of the Guangdong-Hong Kong-Macao Greater Bay Area from January 2015 to December 2021 and the inversion of AOD (Aerosol Optical thickness) products of the three satellite platforms MODIS, SeaWiFS and MISR. In the inversion process, the atmospheric column content AOT was correlated with the surface AOT by using the Goddard Earth Observation System chemical transport model GEOS-Chem, and the comprehensive estimate of PM2.5  in the region was obtained. By considering the interannual variation between ground-based and non-ground-based observation data, the PM2.5 concentration at the grid points was extrapolated to 2000, so as to obtain the long-term series PM2.5  grid point data from 2000 to 2016, with a spatial    resolution     of    0.01°     ×    0.01° .     Based    on     satellite     O3       products     from https://www.temis.nl/protocols/O3total.php,  the  time  range  is  between  January  2015  and December 2021, the spatial resolution of 0.25 ° x 0.25 ° . The above satellite data is processed using MeteoInfo software(http://meteothink.org/).

3.3 analytical method

(1) Kriging interpolation method. Kriging interpolation method is based on variance function  theory and structural analysis, and estimates the attribute value of any point in space according  to the observed values of several discrete points known in space (OLIVER & WEBSTER, 1990). The  spatial  distribution of pollutants was analyzed by Kriging interpolation for the mass  concentration data of each pollutant station in the study area.

(2) Spearman rank coefficient method. Spearman rank coefficient correlation method is often used to evaluate the change rule of various pollutants over time, to quantitatively analyze the change  trend  of  various  pollutants,   and  to   evaluate  the  correlation  between  variables (“Spearman Rank Correlation Coefficient,” 2008). Spearman rank correlation method was used to analyze the spatial variation trend of pollutants, and the correlation between pollutants and meteorological factors and social and economic factors was analyzed.

4 Analysis

4.1 Temporal and spatial distribution characteristics of PM2.5 and O3 concentrations measured on the ground

Using  the  collected  observation  data  from  environmental  monitoring  stations,  the  PM2.5 concentration and pollution level in different cities were plotted over time from 2015 to 2021 to  analyze  their  temporal  and  spatial  distribution  characteristics.  By  calculating  the  daily average PM2.5  concentration of different cities and comparing it with the national ambient air quality standards, different pollution levels are divided.

Ozone has become one of the important atmospheric pollutants in the urban near-ground. The average  daily concentration of O3-8h at ground environmental observation  stations in the Greater Bay Area from 2015 to 2021 was collected and compared with the national ambient air quality standards to reveal the spatial and temporal distribution characteristics and pollution levels of ozone.

Figure 2 Spatial distribution of mean O3 concentration from 2015 to 2021

Figure 3 Spatial distribution of mean PM2.5  concentration from 2015 to 2021

4.2 Spatial and temporal distribution characteristics of PM2.5 andO3 concentrations based

on satellite data

Based on the spatial and temporal distribution characteristics of PM2.5  and O3  concentrations  measured on the ground, the above content is based on the daily mean concentrations of PM2.5  and O3-8h observed at stations from 2015 to 2021 to analyze the characteristics of the inter-  annual changes and spatial differences of pollution concentrations and pollution levels. It is a  specific description of the pollution situation of single station, single element and hour by hour.  In  order  to  conduct  a   more  comprehensive  and  integrated  survey  of  the  atmospheric  environment  of  the  whole  GBA  and  study  and  analyze  the  spatio-temporal  evolution  characteristics of air pollution on a longer time scale, relevant satellite products are introduced  and special remote sensing data processing software or platform (ENVI) is used for data pre-  processing, including radiometric calibration, atmospheric correction, geometric correction, etc. In order to eliminate the atmospheric, terrain and other factors on the observation data. GIS  technology is used to interpolate andresample the processed satellite data, generate the spatial  data  corresponding  to  the  ground  monitoring  station,  and  draw  the  spatial  and  temporal  distribution map.

4.3 Difference comparison between ground observation station data and remote sensing monitoring data

First, for each ground monitoring station, the corresponding satellite data coverage area is found to ensure the consistency of the space time period. Spearman rank coefficient was used to evaluate the correlation between ground measured data and satellite data to determine the strength of the correlation between them. Then, the spatial distribution characteristics obtained from the two data sources are compared to identify the consistency and difference.

4.4 Comparison of changes in air data and the impact factors.

Trends and anomalies in the process of obtaining PM2.5 and O3 concentrations from the air data will be extracted. In an effort to determine what is influencing the air changes, we will compare additional data from the same time period, including wind data, traffic data, and the implementation of new policies. By way of illustration, we postulate that there will be a transient  fluctuation  in  air  quality,  characterised  by  elevated  levels  of  PM2.5  and  O3, throughout the National Day holiday period, when the volume  of travellers  significantly increases.

4.5 Limitations of Analysis

(1) Data reliability: Ground measurement data may be affected by factors such as uneven distribution of monitoring points, equipment failure or maintenance. Satellite data may have limitations in terms of image quality, cloud cover and atmospheric correction. These factors may produce some error in the analysis results.

(2) Satellite data resolution limitations: Satellite data resolution is low, which can lead to local pollution hotspots or spatial changes that cannot be captured at smaller geographic scales. Therefore, for a specific region or city-level analysis, other high-resolution data sources may need to be introduced to improve the accuracy of the analysis.

(3) Time constraints: For long-term trend analysis, longer time series data are needed to draw reliable conclusions. At the same time, because the atmospheric environment is affected by many factors, the analysis of a single time period may not be able to fully capture long-term trends such as seasonal and interannual changes.

5 Expected Outcomes and Significance

Through the analysis of the spatial and temporal distribution characteristics of PM2.5  and O3 concentrations  based  on  ground  measured  data  and  satellite  data,  it  is  expected  that  the concentration distribution trend, hot spot areas and pollution level changes in different time periods and spatial locations can be obtained. These analysis results have important guiding significance for understanding the degree of air pollution in the Greater Bay Area, the spatial distribution of pollution sources, and the formulation and adjustment of related policies. At the same time, comparative analysis can verify the comparability of ground measured data and satellite data, and provide a reference for further research and monitoring to improve the quality of atmospheric environment and protect people's health.