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Abstract China's rapid urbanization has aggravated its air pollution problems and subsequently results in increased interests in the relationship between urbanization and PM 2.5 concentrations. Currently, research on the ways that urbanization directly influences air quality are comparatively mature; however, quantification of the spillover effect of urbanization on PM 2.5 concentrations in adjacent regions remains understudied. Additionally, urbanization is a multi-dimensional phenomena in China and it is important to distinguish the ways in which the multiple aspects of urbanization influence PM 2.5 concentrations. Taking the Beijing-Tianjin-Hebei Urban agglomeration of China as an example, this study explored the spatio-temporal change and spatial dependence pattern of PM 2.5 concentrations, and further examined and compared the unique impacts of the multiple dimensions of urbanization on PM 2.5 concentrations. The results showed that spatial dependence of PM 2.5 generally took place over the range of 200 km in the study area. Economic urbanization had a much stronger influence on air quality than land urbanization or population urbanization; it was determined to be the best indicator to represent the impact of urbanization, especially the spillover effect of urbanization on PM 2.5 concentrations in adjacent regions. Over the period 2000–2010, the impacts from both local and neighboring economic urbanization on PM 2.5 concentrations increased significantly. In contrast, land urbanization mainly exerted influence on air quality in focal regions. Population urbanization showed no significant direct or spillover effects on PM 2.5 concentrations in the whole urban agglomeration. This study provides evidence that the influence of urbanization within and across cities must be considered when conducting joint air pollution control in an urban agglomeration. PM 2.5 taxes based on the diffusion and transfer of environment pressures are one possible policy mechanism that could help to account for the spillover effect of economic urbanization on air quality. System-based approaches and flow-centered governance are two suggested ways to coordinate the management of both urbanization and air quality. Highlights • Spatial dependence of PM 2.5 concentrations occurs over the range of 200 km in BTH. • Spillover effect of urbanization on PM 2.5 occurs through various approaches. • Development of economic, land, population urbanization is inconsistent in BTH. • Economic urbanization can best indicate the impact of urbanization on PM 2.5. [ABSTRACT FROM AUTHOR]

[Display omitted] • Vegetation coverage increased along with urbanization in PRD during 2000–2012. • Relationship between vegetation and urbanization varied at 1−20 km scales. • Mean local variance was used to identify appropriate analysis scale. • NTL-LAI appropriate analysis scale was mainly affected by urbanization level. Accurate quantification of the relationship between urbanization level and vegetation coverage can provide scientific support for urban sustainable development. However, the interaction between them across multi-scales is under-researched because of its complexity. Based on climate change (i.e. precipitation, temperature and solar radiation), partial correlation relationship between urbanization level (nighttime light, NTL) and vegetation coverage (leaf area index, LAI) at different scales (1–20 km) in the Pearl River Delta (PRD) was explored. Mean local variance was used to determine the appropriate scale of the relationship, at which these metrics could have the minimum information missing. The consistency in appropriate analysis scales of NTL, LAI, and NTL-LAI, was quantified to identify which factor more influenced the appropriate analysis scale of NTL-LAI correlation. The results showed from 2000 to 2012, the urbanization level increased at the rate of 0.58/yr and the average LAI increased slowly at the rate of 0.02 m2/m2/yr. The partial correlation between NTL and LAI depicted that urbanization promoted vegetation coverage in the PRD. Meanwhile, the appropriate analysis scale of NTL-LAI correlation was more determined by the analysis scale of urbanization level, than that of vegetation coverage. This study revealed the scale effect of the relationship between urbanization level and vegetation coverage, and identified the determine factor of appropriate analysis scale of the relationship, which could help for the scale selection in related studies. [ABSTRACT FROM AUTHOR]

Irrigated agriculture has had an enormous influence on food security, water security and human well-being. Water footprint (how much water is used), water scarcity (how scarce water is), and crop water productivity (how much productivity irrigation adds) are important indicators for evaluating sustainability in irrigated agriculture. Yet these interrelated indicators have not been studied simultaneously at the county level – the basic administrative unit of agricultural planning and water management in countries such as China, India and Japan. To fill this knowledge gap, we performed a demonstration in China's major crop production region, the North China Plain (NCP)'s 207 counties from 1986 to 2010. The results show that the irrigated agriculture's annual water footprint in the North China Plain increased from 53 billion m3 in 1986 to 78 billion m3 in 2010. All counties faced water scarcity during 1986-2010 even as the average crop water productivity increased from 0.90 kg m−3 to 1.94 kg m−3. There are 173 NCP counties suffering severe water scarcity but still producing significant crop yield with a high water footprint, a red flag of unsustainable irrigated agriculture. This study has implications for revealing potential unsustainable conditions in irrigated agriculture worldwide. • Annual water footprint from irrigated agriculture in the whole North China Plain (207 counties) increased over time. • All counties in the North China Plain faced severe water scarcity. • Spatial-temporal dynamic of crop water productivity in 207 counties was calculated. • We developed water consumption method for calculating water footprint. [ABSTRACT FROM AUTHOR]