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Based on the standardized Euclidean distance method, an evaluation theory for sustainable agricultural development was constructed, which includes a sustainable agricultural development model, an evaluation function model, a sustainable agricultural development exponent (SADE), a rank of the sustainable agricultural development, an index frame of the sustainable agricultural development, a goal system, and a zero system. Data from Zhenyuan County of Gansu province (ZYC) between 2000 and 2012 was used as a case study of the theory. The SADE of ZYC was [0.34, 0.90], and the agriculture system exhibited low sustainability from 2000 to 2003, mid sustainability from 2004 to 2007, and high sustainability from 2008 to 2012. The results explained the actual ZYC situation, and showed that the Euclidean distance method of sustainable agricultural development is feasible. The innovativeness and merits of the method include: (1) reliable methodology, (2) construction using goal and zero systems, which serve as reference systems of sustainable agricultural development and allow control of the evaluation, and (3) index weights embedded in the evaluation function, which prevents errors or mistakes in the expert scoring method and prevents the reassignment of index weights due to adding or subtracting indicators. The main reasons that the SADE increased in ZYC include suitable chemical fertilization and pesticide application, enhancement of conservation farming areas, soil testing and fertilizer-formulated areas, and drinking water security in rural and non-disaster crop areas. The main problems with increasing the SADE in ZYC include straw application and low farmer income, both of which should be increased. [ABSTRACT FROM AUTHOR]

As a primary air pollutant, fine particulate matter (PM 2.5) is increasingly attracting attention. Crowdsourcing observations based methods are thought to be the best solutions for identifying the spatio-temporal distribution of PM 2.5 in intra-urban areas. However, inconsistent timing in the collection of crowdsourced data has typically been ignored in previous studies. To address this issue, a temporally calibrated method (TCM) was introduced in this study. By interpolating TCM-estimated observations using the inverse distance weighted (IDW) method, variations of PM 2.5 concentrations across the urban areas of Changsha City were captured. The results demonstrate that TCM can efficiently resolve the inconsistent timing defects of raw crowdsourcing observations (R 2 was 0.73 and the RMSE was 7.65 μg/m3). Furthermore, PM 2.5 distributions developed using TCM-based interpolations are of a finer spatial scale than those developed from raw observations at crowdsourcing locations. With a lack of funds to build sufficient stationary monitoring sites, developing crowdsourcing observation-based technology is the most promising solution for revealing intra-urban PM 2.5 variations at a higher spatio-temporal- resolution. Image 1 • A temporal calibration method was introduced for crowdsourcing air quality data. • TCM can eliminate time inconsistence of original crowdsourcing based PM 2.5 concentrations. • Intra-urban variations of PM 2.5 concentrations were revealed by densely observation. • Crowdsourcing observation is effective for fine scale air quality mapping in urban area. [ABSTRACT FROM AUTHOR]