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.

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.

Methods for estimating the spatial distribution of PM2.5 concentrations have been developed but have not yet been able to effectively include spatial correlation. We report on the development of a spatial back-propagation neural network (S-BPNN) model designed specifically to make such correlations implicit by incorporating a spatial lag variable (SLV) as a virtual input variable. The S-BPNN fits the nonlinear relationship between ground-based air quality monitoring station measurements of PM2.5, satellite observations of aerosol optical depth, meteorological synoptic conditions data and emissions data that include auxiliary geographical parameters such as land use, normalized difference vegetation index, elevation, and population density. We trained and validated the S-BPNN for both yearly and seasonal mean PM2.5 concentrations. In addition, principal components analysis was employed to reduce the dimensionality of the data and a grid of neural network models was run to optimize the model design. The S-BPNN was cross-validated against an analogous but SLV-free BPNN model using the coefficient of determination (R2) and root mean squared error (RMSE) as statistical measures of goodness of fit. The inclusion of the SLV led to demonstrably superior performance of the S-BPNN over the BPNN with R2 values increasing from 0.80 to 0.89 and with the RMSE decreasing from 8.1 to 5.8 μg/m3. The yearly mean PM2.5 concentration in China during the study period was found to be 41.8 μg/m3 and the model estimated spatial distribution was found to exceed Level 2 of the China Ambient Air Quality Standards (CAAQS) enacted in 2012 (>35 μg/m3) in more than 70% of the Chinese territory. The inclusion of spatial correlation upgrades the performance of conventional BPNN models and provides a more accurate estimation of PM2.5 concentrations for air quality monitoring.

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