大气污染物通常是指由于人类活动或自然过程排入大气，并对人和环境造成有害影响的物质。随着我国城市化进程的加快，城市扩张所引发的大气污染等一系列环境问题，已成为制约城市系统健康、平稳、有序发展的重要因素之一。尤其近年来，在“一带一路”等国家战略支持下，关中平原城市群地区经济高速发展的同时，其所造成的大气污染问题也日益严峻。但目前我国针对大气污染的监测，大多还是依靠分布稀疏且不均匀的国控监测站，这给大气污染物浓度空间分布特征分析及人口暴露风险评估等造成一定影响。因此，开展关中平原城市群地区大气污染物浓度的模拟研究具有重要意义，通过模拟结果也可更加全面的分析关中平原城市群大气污染物时空变化特征，为该地区大气污染防治及人口暴露风险评估提供科学合理的建议。

本文选取2017年关中平原城市群内54个国控空气质量监测站的实时监测数据，并结合气象、地形、土地利用、植被指数、道路交通、工业污染源、人口密度及夜间灯光等多源数据，构建LUR模型，模拟关中平原城市群大气污染物浓度的空间分布情况，并根据最佳模拟结果对关中平原城市群人口PM_2.5暴露风险进行评估及驱动力分析。主要研究内容及结论如下：

（1）基于最小二乘(OLS)、地理加权(GWR)算法，分别构建不同季节、不同种类的大气污染物LUR模型，对研究区大气污染物浓度的空间分布进行模拟。结果表明：①关中平原城市群大气污染物浓度受地形的影响最为显著；②各模型的R²_adj均在0.7以上，可对该地区6种主要大气污染物浓度进行有效模拟；③通过模型适用性及精度对比发现，两类模型均更适用于污染严重的秋冬季，以及颗粒污染物PM_2.5、PM₁₀浓度的模拟。相比于OLS-LUR模型，GWR-LUR模型的R²_adj、线性拟合度R²分别提高了2.54%、2.49%，平均相对误差(MRE)、均方根误差(RMSE)分别降低了19.88%、20.35%，AICc及Res Moran’sⅠ值也显著减小，模型性能总体得到提高；④模拟结果图显示，关中平原城市群6种主要大气污染物浓度空间分布大致相同，浓度高值多集中在汾渭平原等低海拔地区，浓度低值则多集中在秦岭山地等高海拔地区，总体呈现出东部北部高、西部南部低的特征，模拟结果与实际情况相吻合。

（2）基于最佳的PM_2.5浓度模拟结果，结合人口密度数据，分别从PM_2.5浓度、人口暴露强度、人口加权PM_2.5浓度等3项指标，全面评估关中平原城市群人口PM_2.5暴露风险。结果表明：①PM_2.5浓度指标下，关中平原城市群人口PM_2.5暴露风险总体呈现东高西低的特征；②人口暴露强度指标下，关中平原城市群人口PM_2.5暴露风险分布特征与人口密度分布高度相似，各市、县中心城区为高暴露风险区。风险分级显示，研究区有约10%的地区处于高风险等级水平，且零星分布在“临汾—运城—关中”一线。从城市尺度分析，各城市的人口PM_2.5暴露风险差异性明显，西安为暴露风险最高的城市，其高风险等级区面积占比超过70%；③人口加权PM_2.5浓度指标下，关中平原城市群PM_2.5污染对居民的健康危害和实际影响要远大于其平均浓度水平，且人口明显集中于PM_2.5浓度高值区。结合空间自相关检验可知，咸阳、西安、晋陕交界为高暴露风险聚集区，对这些城市及地区应进行重点防控。

（3）基于地理探测器的因子探测功能，对关中平原城市群人口PM_2.5暴露风险的驱动因素进行分析。结果表明：关中平原城市群人口PM_2.5暴露风险的空间分异受自然及社会经济因素的共同影响。其中自然因素的影响程度依次为：高程＞植被指数＞降水＞气温＞相对湿度；社会经济因素的影响程度依次为：人口密度＞第二产业占比＞工业废气排放量＞机动车保有量＞GDP＞人均能源消耗量＞建成区面积。整体看来，人类活动是造成关中平原城市群人口PM_2.5暴露风险分异的主要驱动力，其次受高程因子的影响。

Air pollutants generally refer to substances that are discharged into the atmosphere due to human activities or natural processes and have harmful effects on people and the environment. With the acceleration of my country's urbanization process, a series of environmental problems such as air pollution caused by urban expansion have become one of the important factors restricting the healthy, stable and orderly development of the urban system. Especially in recent years, with the support of national strategies such as the “One Belt, One Road”, while the rapid economic development of the Guanzhong Plain urban agglomeration area, the problem of air pollution caused by it has become increasingly severe. However, the current monitoring of air pollution in my country mostly relies on sparsely and unevenly distributed nationally controlled monitoring stations, which has a certain impact on the analysis of the spatial distribution of air pollutant concentrations and the assessment of population exposure risks. Therefore, it is of great significance to carry out a simulation study on the concentration of air pollutants in the Guanzhong Plain urban agglomeration. The simulation results can also more comprehensively analyze the temporal and spatial characteristics of air pollutants in the Guanzhong Plain urban agglomeration, and provide air pollution control and population exposure risk evaluation Provide scientific and reasonable suggestions.

This paper selects the real-time monitoring data of 54 state-controlled air quality monitoring stations in the Guanzhong Plain urban agglomeration in 2017, and combines multi-source data such as meteorology, topography, land use, vegetation index, road traffic, industrial pollution sources, population density, and night lights. The LUR model is constructed to simulate the spatial distribution of atmospheric pollutant concentrations in the Guanzhong Plain urban agglomeration, and the PM_2.5 exposure risk of the population in the Guanzhong Plain urban agglomeration is evaluated and driving force analysis based on the best simulation results. The main research contents and conclusions are as follows:

(1) Based on the least squares (OLS) and geographic weighting (GWR) algorithms, build LUR models of air pollutants in different seasons and types, and simulate the spatial distribution of air pollutant concentrations in the study area. The results show that: ①The concentration of air pollutants in the Guanzhong Plain urban agglomeration is most significantly affected by the terrain; ②The R²_adj of each model is above 0.7, which can effectively simulate the concentration of six major air pollutants in the region; ③The applicability of the model and Accuracy comparison shows that both types of models are more suitable for the severely polluted autumn and winter, as well as the simulation of the concentration of particulate pollutants PM_2.5 and PM₁₀. Compared with the OLS-LUR model, the R²_adj and linear fit R² of the GWR-LUR model are increased by 2.54% and 2.49%, respectively. MRE and RMSE are reduced by 19.88% and 20.35%, respectively. The values of AICc and Res Moran’s I are also significantly reduced, and the overall model performance is improved. ④The simulation result graph shows that the spatial distribution of the six major air pollutants in the Guanzhong Plain urban agglomeration is roughly the same. The high concentrations are mostly concentrated in low-altitude areas such as the Fenwei Plain, while the low concentrations are more. Concentrated in high-altitude areas such as the Qinling Mountains, it generally presents the characteristics of high in the north of the east and low in the south of the west. The simulation results are consistent with the actual situation.

(2) Based on the best simulation results of PM_2.5 concentration, combined with population density data, a comprehensive assessment of the PM_2.5 exposure risk of the population in the Guanzhong Plain urban agglomeration from three indicators: PM_2.5 concentration, population exposure intensity, and population-weighted PM_2.5 concentration. The results show that: ①Under the PM_2.5 concentration index, the PM_2.5 exposure risk of the population in the Guanzhong Plain urban agglomeration is generally higher in the east and lower in the west; ②Under the index of population exposure intensity, the distribution characteristics of PM_2.5 exposure risk of the population in the Guanzhong Plain urban agglomeration are highly similar to the distribution of population density, and the central urban areas of cities and counties are high exposure risk areas. The risk classification shows that about 10% of the study area is at a high risk level, and they are scattered in the front line of "Linfen-Yuncheng-Guanzhong". From the analysis of the city scale, the population PM_2.5 exposure risk of each city is obviously different. Xi'an is the city with the highest exposure risk, and its high-risk area accounts for more than 70%; ③Under the population-weighted PM_2.5 concentration index, the Guanzhong Plain The health hazards and actual impacts of PM_2.5 pollution in urban agglomerations are far greater than their average concentration levels, and the population is obviously concentrated in areas with high PM_2.5 concentrations. Combined with the spatial autocorrelation test, it can be known that the borders of Xianyang, Xi'an, and Shanxi-Shaanxi are high-risk areas, and these cities and regions should be focused on prevention and control.

(3) Based on the factor detection function of the geographic detector, analyze the driving factors of the PM_2.5 exposure risk of the population in the Guanzhong Plain urban agglomeration. The results show that the spatial differentiation of PM_2.5 exposure risk of the population in the Guanzhong Plain urban agglomeration is jointly affected by natural and socio-economic factors. The order of influence of natural factors is as follows: elevation>vegetation index>precipitation>temperature>relative humidity; the order of influence of socioeconomic factors is: population density>proportion of secondary industry>industrial waste gas emissions>vehicle ownership>GDP>Per capita energy consumption>Built-up area. On the whole, human activities are the main driving force that causes the population PM_2.5 exposure risk differentiation of the Guanzhong Plain urban agglomeration, followed by the influence of the elevation factor.

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