A GIS-based spatial correlation analysis for ambient air pollution and AECOPD hospitalizations in Jinhua, China

Background : Acute exacerbations of COPD (AECOPD) are important events during disease procedure. AECOPD have negative effect on patients’ quality of life, symptoms and lung function, and result in high socioeconomic costs. Though previous studies have demonstrated the significant association between outdoor air pollution and AECOPD hospitalizations, less is known about the spatial relationship utilized a spatial analyzing technique- Geographical Information System (GIS). Objective : Using GIS to investigate the spatial association between ambient air pollution and AECOPD hospitalizations in Jinhua City, 2019. Methods : 1563 AECOPD hospitalization cases in Jinhua, 2019 were enrolled in our analysis. Monthly concentrations of six monitored six pollutants (SO 2 , PM10, PM2.5, NO 2 , CO, O 3 ) during January 2019 − December 2019 were provided by Environmental Protection Agency of Jinhua City. Everyone was geocoded in ArcGIS10.5 software. The spatial distribution of six pollutants and the temporal-spatial speciﬁc air pollutants exposure level for everyone was estimated by ordinary Kriging model. Spatial autocorrelation (Global Moran’s I) was employed to explore the spatial association between ambient air pollutants and AECOPD hospitalizations. A generalized linear model (GLM) using a Poisson distribution with log-link function was used to construct a core model. Results : The concentrations of SO 2 , PM10, PM2.5, NO 2 , CO, O 3 and AECOPD hospitalization cases showed statistical signiﬁcant spatially clustered at residence. The Z-score of SO 2 , PM10, PM2.5, NO 2 , CO, O 3 at residence is 31.88, 42.95, 45.90, 32.29, 52.18, and 34.59, respectively. After adjusting for potential confounders in the model, the SO 2 , NO 2 and O 3 concentrations at residence showed statistical signiﬁcance increase of hospitalizations due to AECOPD. Conclusions : Ambient air pollution is correlated with AECOPD hospitalizations spatially. The SO 2 , NO 2 and O 3 concentrations at residence was significantly associated with an increase of hospitalizations due to AECOPD in Jinhua, 2019.

experience an acute worsening of respiratory symptoms that results in additional therapy; this event is defined as a COPD exacerbation (AECOPD) [2]. AECOPD bring negative effect on patients' quality of life, symptoms and lung function, and result in high socioeconomic costs [3]. Up to 25% of AECOPD events require hospitalization [4]. In China the median cost of inpatient care for AECOPD was CN ¥11,598, with medication costs contributing to 36-88% of costs, depending on the tier level of the hospital [5]. Exacerbations of COPD can be triggered by various factors. Increasing evidence support that ambient air pollution is an environmental triggering factor for AECOPD [6]. Since China's economic reforms plan, large emissions from energy consumption are concentrated in the megacity clusters, such as Beijing-Tianjin-Hebei (BTH), the Pearl River delta (PRD), and the Yangtze River delta (YRD) regions. Jinhua city is located in the YRD region of East China, in the middle of Zhejiang province [7].
The health effects of inhalable particulate matter (aerodynamic diameter < 10 µm, PM 10), (aerodynamic diameter < 2.5 µm, PM 2.5), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), carbon monoxide (CO), and ozone (O 3 ) on COPD are well established [8][9][10][11]]. Geographical information system (GIS) refers to computer-based programs to collect, store, retrieve, and statistically manipulate geographic or location-based information [12]. Better interpretation of the patterns, trends and relationships between disease and demography, environment, space and time could be obtained through GIS. Therefore, GIS has important application in medical and health area, especially in the application of etiological research [13,14]. Wang et al. investigated the spatial association between ambient air pollution and AECOPD hospitalizations using GIS [15]. One limitation is lack of information of PM2.5, which was not regularly monitored in the study period due to limited monitoring technique in the year of 2009. China's rapid industrialization and urbanization has led to poor air quality. There is a substantial changes in air pollution across China within the last decades [16].
To our knowledge, few studies employed GIS technique to assess spatio-temporal specific exposure to air pollutants in the spatial association analysis between admissions for AECOPD and air pollution.
There is no similar study in YRD region of East China. We aim to assess the spatial association between ambient air pollution exposure and AECOPD hospitalizations in Jinhua City through GIS.

Study area & study period
We set the study in Jinhua City, 2019. Jinhua (alternately romanized as Kinhwa), is a prefecture-level city in central Zhejiang province in eastern China. It is located in the north-western part of Zhejiang province at 29º.079176 northern latitude and 119º.647423 east of Greenwich.

Target Population
All cases enrolled in our study were interspersed in Jinhua City. Written informed consent was obtained prior to data collection. The study and consent procedure were approved by the Ethics Committee of Jinhua Municipal Central Hospital.
COPD hospitalization cases met with the following inclusion criteria were included in our study: (1) Hospitalization due to acute exacerbations of COPD, identified by International Statistical

Statistical analysis
Global Moran's I statistic was used for spatial autocorrelation analysis in ArcGIS 10.5. Moran's I, p value and Z score were calculated to test the spatially clustered tendency between concentrations of air pollutants and AECOPD hospitalization cases. Confidence level of 99% was selected. Values of P < 0.01 were considered statistically significant. A generalized linear model (GLM) using a Poisson distribution with log-link function was used to construct a core model. Monthly numbers of AECOPD admissions were selected as dependent variable. Age, gender, season of hospitalization, smoking status and air pollutants concentrations at residence and workplace were selected as dependent variables. After controlling for the confounding effects of seasonality, age, gender and smoking status, monthly concentrations of (O 2 , PM10, PM2.5, NO 2 , CO, O 3 at residence for each individual were added to the core model to determine the increase of COPD admissions for a 10 µg/m 3 increase in each of these air pollutants. An interaction effect between gender (male or female) and smoking status (ex-smoker or non-smoker or current smoker) were analyzed in the model. Values of P < 0.05 were considered statistically significant. All the analysis was performed with SPSS 25.0 software.

Spatial Distribution Of Aecopd Admissions
The acute exacerbations accelerate the rate of decline of lung function correlated with significant mortality, particularly in those requiring hospitalizations [17].

Predicting Model
A generalized linear model (GLM) using a Poisson distribution with log-link function was used to construct a core model. Results of the core model were shown in Table 4. After adjusting for potential confounders in the model, only the SO 2 , NO 2 and O 3 concentrations at residence showed statistical significance, with an increase of hospitalizations for acute exacerbations of COPD.

Discussion
In recent years, air pollution has become a worldwide environmental issue, and China is facing the greatest challenge from deteriorating air quality. It is of great importance and urgency to find out the definite impact of air pollution. Our study assessed the spatial correlation of ambient air pollution Though previously descriptive retrospective study have reported the associations between ambient air pollution and COPD hospitalizations [18], and less knowledge was known on the spatial correlation between ambient air pollution and AECOPD hospitalization cases. More evidence is needed to demonstrate the solid association between ambient air pollution and AECOPD hospitalizations. In the association study between O 3 and COPD admissions, several previous studies have demonstrated the association. In APHEA project conducted in Europe, O 3 was associated with daily admissions for COPD, the relative risks (RR) for a 50 mg/m 3 increase in daily mean concentrations of O 3 was 1.04 (1.02, 1.07) [19]. In the research conducted in Hong Kong, associations between O 3 and COPD hospitalizations were convinced both in single-pollutant model and multipollutant model [20].
Sauerzapf's study on a rural county of England showed that 10 µg/m3 increase in CO was associated with a 2% increase in the odds of admission, but no associations were observed with O 3 or particulates. [21]. Current studies support the role of SO 2  PM2.5 and AECOPD hospitalization, which added new evidence to the association (especially spatial association) between ambient air pollution and AECOPD hospitalization. As a spatial data processing tool, GIS showed novel and great potential on air pollutants exposure assessment and spatial analysis in AECOPD research.
One limitation is the cross-sectional nature of the study, which could not demonstrate the causal relationship between ambient air pollution and AECOPD hospitalization. As the monitoring technique developing, further study could be done to better illustrate the association between ambient air pollution and admissions due to AECOPD in East China. Prospective cohort study was also needed to illustrate the causal relationship in the future.

Conclusion
Ambient air pollution is spatially correlated with AECOPD hospitalizations. At residence, SO2 is of greatest spatial correlation with AECOPD hospitalization. After adjusting for potential confounders in the model, SO 2 , O 3 and NO 2 concentrations at residence showed a statistically significant relation with the increase of hospitalizations for acute exacerbations of COPD. As a spatial data processing tool, GIS has novel and great potential on air pollutants exposure assessment and spatial analysis in

Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate
The study protocol was approved by the institutional review board and ethics committee of Jinhua municipal central hospital and was conducted according to the principles of the Declaration of Helsinki. The need to obtain informed consent was waived due to the retrospective nature of the study.