The international journal of Aerosol and Air Quality Research (AAQR) covers all aspects of aerosol science and technology, atmospheric science and air quality related issues.

Air pollution is one of the key environmental problems associated with urbanization and land use. Taking Wuhan city, Central China, as a case example, we explore the quantitative relationship between land use (built-up land, water bodies, and vegetation) and air quality (SO₂, NO₂, and PM₁₀) based on nine ground-level monitoring sites from a long-term spatio-temporal perspective in 2007–2014. Five buffers with radiuses from 0.5 to 4 km are created at each site in geographical information system (GIS) and areas of land use categories within different buffers at each site are calculated. Socio-economic development, energy use, traffic emission, industrial emission, and meteorological condition are taken into consideration to control the influences of those factors on air quality. Results of bivariate correlation analysis between land use variables and annual average concentrations of air pollutants indicate that land use categories have discriminatory effects on different air pollutants, whether for the direction of correlation, the magnitude of correlation or the spatial scale effect of correlation. Stepwise linear regressions are used to quantitatively model their relationships and the results reveal that land use significantly influence air quality. Built-up land with one standard deviation growth will cause 2% increases in NO₂ concentration while vegetation will cause 5% decreases. The increases of water bodies with one standard deviation are associated with 3%–6% decreases of SO₂ or PM₁₀ concentration, which is comparable to the mitigation effect of meteorology factor such as precipitation. Land use strategies should be paid much more attention while making air pollution reduction policies.

Many existing gradient analysis methods are arbitrary or too simple in gradient partitioning and unsuitable for cities with irregular forms. We propose an improved gradient analysis method with urban structural features as spatial constraints to properly partition an urban area into more homogeneous buffers. Taking the Wuhan metropolitan area in China as an example, we use the improved gradient partitioning method and six landscape metrics to characterize urban expansion and green space fragmentation in 1989, 2001, and 2013 and investigate the relationship between urban green fragmentation and urban expansion using correlation analysis and regression modeling. The results show that the fragmentation of built-up area in each buffer generally decreases from 1989 to 2013, but the fragmentation of green space increases. For each time point, with growing distance from urban centers, the fragmentation of built-up area increases and green space fragmentation decreases. This shows that urbanization exerts a significant influence on fragmentation of urban green space from both a temporal and a spatial perspective. Intensive urban expansion moved outward mainly at the cost of agricultural land and resulted in more fragmented green space in the urban fringe. Further quantitative analyses show that the area ratio and fragmentation of the built-up area are statistically negatively correlated with the fragmentation of green space. The improved gradient analysis with spatial constraints can generate more homogeneous ring gradients and then lead to more reasonable results, thereby helping to better understand how urban expansion affects landscape dynamics.

PM_2.5 has become an increasing public concern recently because of its visibility reduction and severe health risks. For the whole year of 2013, hourly PM_2.5 data of 496 monitoring sites scattered in 74 cities of China are collected to analyze temporal and spatial variability of PM_2.5 concentration. Different temporal scales (seasonal variation, monthly variation and daily variation) and spatial scales (urban versus rural, typical areas and national scale) are discussed. Results show that PM_2.5 concentration changes significantly in both long-term and short-term scales. An apparent bimodal pattern exists in daily variation of PM_2.5 concentration and the daytime peak appears around 10:00 am while the lowest concentration appears around 16:00 pm. Spatial autocorrelation analysis and Ordinary Kriging are used to characterize spatial variability. Moran’s I of PM_2.5 concentration in three typical regions, the Beijing-Tianjin-Hebei region, the Yangtze River Delta region and the Pearl River Delta region, is 0.906, 0.693, 0.746, respectively, which indicates that PM_2.5 is strong spatial correlated. Spatial distribution of annual PM_2.5 concentration simulated by Ordinary Kriging shows that 7.94 million km² (83%) areas fail in meeting the requirement of China’s National Ambient Air Quality Standards Level-2 (35 µg/m³) and there are at least three concentrated highly polluted areas across the country.

Optimizing land-use allocation is important to regional sustainable development, as it promotes the social equality of public services, increases the economic benefits of land-use activities, and reduces the ecological risk of land-use planning. Most land-use optimization models allocate land-use using cell-level operations that fragment land-use patches. These models do not cooperate well with land-use planning knowledge, leading to irrational land-use patterns. This study focuses on building a heuristic land-use allocation model (PSOLA) using particle swarm optimization. The model allocates land-use with patch-level operations to avoid fragmentation. The patch-level operations include a patch-edge operator, a patch-size operator, and a patch-compactness operator that constrain the size and shape of land-use patches. The model is also integrated with knowledge-informed rules to provide auxiliary knowledge of land-use planning during optimization. The knowledge-informed rules consist of suitability, accessibility, land use policy, and stakeholders’ preference. To validate the PSOLA model, a case study was performed in Gaoqiao Town in Zhejiang Province, China. The results demonstrate that the PSOLA model outperforms a basic PSO (Particle Swarm Optimization) in the terms of the social, economic, ecological, and overall benefits by 3.60%, 7.10%, 1.53% and 4.06%, respectively, which confirms the effectiveness of our improvements. Furthermore, the model has an open architecture, enabling its extension as a generic tool to support decision making in land-use planning.