Fine particulate matter (PM_2.5) pollution has become one of the greatest urban issues in China. Studies have shown that PM_2.5 pollution is strongly related to the land use pattern at the micro-scale and optimizing the land use pattern has been suggested as an approach to mitigate PM_2.5 pollution. However, there are only a few researches analyzing the effect of land use on PM_2.5 pollution. This paper employed land use regression (LUR) models and statistical analysis to explore the effect of land use on PM_2.5 pollution in urban areas. Nanchang city, China, was taken as the study area. The LUR models were used to simulate the spatial variations of PM_2.5 concentrations. Analysis of variance and multiple comparisons were employed to study the PM_2.5 concentration variances among five different types of urban functional zones. Multiple linear regression was applied to explore the PM_2.5 concentration variances among the same type of urban functional zone. The results indicate that the dominant factor affecting PM_2.5 pollution in the Nanchang urban area was the traffic conditions. Significant variances of PM_2.5 concentrations among different urban functional zones throughout the year suggest that land use types generated a significant impact on PM_2.5 concentrations and the impact did not change as the seasons changed. Land use intensity indexes including the building volume rate, building density, and green coverage rate presented an insignificant or counter-intuitive impact on PM_2.5 concentrations when studied at the spatial scale of urban functional zones. Our study demonstrates that land use can greatly affect the PM_2.5 levels. Additionally, the urban functional zone was an appropriate spatial scale to investigate the impact of land use type on PM_2.5 pollution in urban areas.

Long-term exposure to PM2.5 can lead to great adverse health effect on human health. To better guide public policies that aim to reduce PM2.5 population exposure, this work combined multi-source data to realize high-resolution PM2.5 exposure risk assessment in Nanchang urban region. The land use regression (LUR) model was used to simulate the seasonal-spatial variations of PM2.5 concentrations at 100-m resolution, and building information extracted from IKONOS image was applied to spatialize population at 100-m resolution. An improved piece-wise population exposure approach was introduced to evaluate the exposure risk, and results were compared with two classical approaches. In all seasons, results by the absolute concentration approach are very different from the other two, showing obvious spatial smoothing effect. Results by population-weighted and piece-wise exposure approaches are similar in spring and autumn, and different in summer and winter. In winter, the area and population percentages divided to severity level 7 by population-weighted exposure approach are 5.21% and 2.35% lower than that by piece-wise exposure approach. When in summer, the area and population percentages divided to severity level 7 by population-weighted exposure approach are 6.77% and 24.79% higher than that by piece-wise exposure approach. The absolute concentration approach is disadvantageous for the identification of high-risk areas, the population-weighted exposure approach would underestimate or overestimate the population exposure when air is seriously polluted or remarkably clean, and the proposed piece-wise exposure approach would be more reasonable. The integrated methodology is effective in exposure risk assessment and can be applied to other regions and pollutants.

景观格局演变的大气环境效应是城市大气污染的主要成因，但相关研究尚未形成普适性的景观分类方案。论文引入热岛研究中的局地气候区(local climate zone，LCZ)框架，利用土地利用回归(land use regression，LUR)模型对南昌市主城区进行PM2.5浓度模拟，探索LCZ框架在PM2.5浓度模拟中的适用性。对比传统LUR建模和3种融合LCZ的LUR建模发现：① 融合LCZ的LUR模型调整R²普遍提高，标准估计误差降低，春、夏季效果尤为明显。其中，同时替代土地利用和人口密度变量的建模效果最佳，说明融合LCZ进行PM2.5浓度模拟是可行的，四季均可解释80%以上的PM2.5浓度空间变化。② LCZ 8(大型低层)和LCZ 10(重工业)是替代工业用地的最佳变量，LCZ G(水域)和LCZ F(裸地/沙地)是替代生态用地的最佳变量，LCZ 1~3(紧凑型建筑)和LCZ 5(开敞中层建筑)是替代人口密度的最佳变量，说明LCZ能在一定程度上解释城市PM2.5空间分布的驱动机制。③ 气象因子的季节变化是导致PM2.5季节变化的主要原因，不同季节影响PM2.5浓度的主导气象因子不同，春、秋季节主要受风速的影响，夏季受相对湿度的影响，冬季受温度和降水的影响。研究探索了LCZ框架在PM2.5浓度模拟中的适用性，结果可为城市大气污染评估与治理提供参考。

The urban landscape pattern has critical effects on atmospheric particulate matter (PM) pollution. However, the effects may greatly differ with variations of the spatial scales and seasons, which remains poorly understood. This study established a multiple spatial scale analysis on the impact of the urban landscape pattern on PM pollution across different seasons in Nanchang, China, via regular and redundancy analysis (RDA). Six 2D and six 3D metrics were employed to characterize the landscape pattern with buffer radii of 100, 300, 600, 900, 1,200, 2,400, and 4,800 m centered at monitoring stations. Results showed that the effects of the urban landscape pattern on PM pollution have significant seasonal differences and spatial scale effects. Urban landscape pattern has a stronger influence on PM pollution in fall and winter than in spring and summer. The explanatory ability of selected metrics on PM concentrations first increased, then decreased, and finally increased as the scales increased, and the highest accumulated explanatory ability was observed at the 900 m scale. At smaller scales (buffer radii ≤ 900 m), the ability of 3D metrics was stronger than 2D metrics to explain the PM changes, and at larger scales (buffer radii ≥ 1,200 m), the 2D metrics were more explanatory. Percentage of building landscape (PLAND) and patch cohesion index (COHESION) were the key 2D metrics affecting PM pollution, and spatial congestion degree (SCD) and landscape enclosing degree (LED) were the key 3D metrics. The results provide important implications in urban planning for effective PM pollution mitigation.