Fine particles (PM 2.5 ) and coarse particles (PM 2.5-10 ) are generally produced by different sources, so the PM 2.5 /PM 10 ratio reveals characteristics of particle pollution. The ratio can be used to characterize the underlying atmospheric processes and evaluate historical PM 2.5 pollution in absence of direct measurements. However, application of the ratio needs its varying pattern because PM concentrations change significantly at time and space. Hourly PM 2.5 and PM 10 observations at nine monitoring sites in urban area (Urban-sites) and one remote Background-site in Wuhan in 2013-2015 were collected to investigate both long-term, short-term temporal variation and spatial distribution, spatial disparity of the ratio at a city scale. The results show that annual average PM 2.5 /PM 10 ratio is 0.62 at Urban-sites and 0.68 at Background-site with apparent seasonal, monthly and daily variations. The ratio reaches the maximum in winter because of stable atmospheric conditions. There are apparent night-day differences of daily variation of the ratio, which increases at night in all seasons in consequence of temperature inversion and declines in the daytime with a moderate rise in the afternoon. We find obvious spatial gradients of the ratio that gradually increases from urban core to urban fringe and to suburban. This study provides further insights to the spatio-temporal variability of PM 2.5 /PM 10 ratio. The evidence indicates that the variability of PM 2.5 /PM 10 should be noticed in its applications.

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The lockdown of cities to against the COVID-19 epidemic directly decreases urban socioeconomic activities. Remotely sensed night-time light (NTL) provides a macro perspective to capture these variations. Here, taking 20 global megacities as examples, we adopted the NASA’s Black Marble NTL data with a daily resolution to investigate their spatio-temporal changes. We collected daily NTL products for four weeks (one month) before and after the date of lockdown in each city, which were then summarized as weekly and monthly averaged NTL images after pre-processing (cloud removing, outlier detection, etc.). Results show that NTL overall decreased after the lockdown of cities, but with regional disparities and varying spatial patterns. Asian cities experienced the most obvious reduction of NTL. Particularly, the monthly averaged NTL in Mumbai, India, decreased by nearly 20% compared to one month before. However, there were no significant decline in NTL in European cities. African cities also experienced stable changes of NTL. Spatially, city centers darkened more obviously than the urban periphery. Facing emergencies, NTL data has broad applications in monitoring socioeconomic dynamics and assessing public policies in a near real-time manner.

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 (SO 2 , NO 2 , and PM 10 ) 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 NO 2 concentration while vegetation will cause 5% decreases. The increases of water bodies with one standard deviation are associated with 3%-6% decreases of SO 2 or PM 10 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.

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