China's soil pollution is much worse than previously thought (“China gets serious about its pollutant-laden soil,” C. Larson, News & Analysis, 28 March, p. [1415][1]). A report released on 17 April admitted that 16.1% of the Chinese soil was polluted, including 19.4% of farmland, 10.0% of forest land, 10.4% of grassland, and 11.4% of unused land ([ 1 ][2]). Of the contaminated soil samples, 82% contained toxic inorganic pollutants, the most common being heavy metals such as cadmium, mercury, arsenic, chromium, and lead, which can cause chronic health problems. Moreover, levels of organic pollutants such as dichlorodiphenyltrichloroethane (DDT), polycyclic aromatic hydrocarbons (PAHs), and hexachlorocyclohexanes (HCHs) are also very high, and samples above established safety levels account for 1.9%, 1.4%, and 0.5%, respectively, of all tested soil samples, which covered 6.3 million square kilometers. Aside from industrial plant waste and mining operations, the report states that the unsustainable use of chemical fertilizers and pesticides is a main human cause of widespread soil pollution ([ 1 ][2]). China's food production is affected by soil pollution but also creates much pollution itself. China consumes nearly one-third of the world's fertilizer, and the pesticide usage per unit area is 2.5 times the world average ([ 2 ][3]). The quality of cultivated land should be guaranteed. Farmers should be subsidized to improve fertilizer use efficiency and encouraged to adopt organic and biodynamic farming methods that are not reliant on heavy input of chemicals. Soil remediation projects should be implemented to improve the polluted soil gradually. Finally, there is a need to change cultural habits relating to consumption so as to reduce food waste nationwide. ![Figure][4] Contamination below. A recent report found soil pollution in 19.4% of China's farmlands. CREDIT: ALBERTHERRING/WIKIMEDIA COMMONS 1. [↵][5] Ministry of Environmental Protection and Ministry of Land and Resources of P.R. China, “Reports on China's Soil Pollution Survey” (2014); [www.zhb.gov.cn/gkml/hbb/qt/201404/t20140417_270670.htm][6] [in Chinese]. 2. [↵][7] “Establish a long-term mechanism for sustainable agricultural development,” China People's Daily (22 January 2014); [http://paper.people.com.cn/rmrb/html/2014-01/22/nw.D110000renmrb\_20140122\_1-02.htm#fin][8] [in Chinese]. [1]: /lookup/doi/10.1126/science.343.6178.1415 [2]: #ref-1 [3]: #ref-2 [4]: pending:yes [5]: #xref-ref-1-1 "View reference 1 in text" [6]: http://www.zhb.gov.cn/gkml/hbb/qt/201404/t20140417_270670.htm [7]: #xref-ref-2-1 "View reference 2 in text" [8]: http://paper.people.com.cn/rmrb/html/2014-01/22/nw.D110000renmrb_20140122_1-02.htm#fin

This paper analyses the spatio-temporal trends and variability in annual, seasonal, and monthly rainfall with corresponding rainy days in Bhilangana river basin, Uttarakhand Himalaya, based on stations and two gridded products. Station-based monthly rainfall and rainy days data were obtained from the India Meteorological Department (IMD) for the period from 1983 to 2008 and applied, along with two daily rainfall gridded products to establish temporal changes and spatial associations in the study area. Due to the lack of more recent ground station rainfall measurements for the basin, gridded data were then used to establish monthly rainfall spatio-temporal trends for the period 2009 to 2018. The study shows all surface observatories in the catchment experienced an annual decreasing trend in rainfall over the 1983 to 2008 period, averaging 15.75 mm per decade. Analysis of at the monthly and seasonal trend showed reduced rainfall for August and during monsoon season as a whole (10.13 and 11.38 mm per decade, respectively); maximum changes were observed in both monsoon and winter months. Gridded rainfall data were obtained from the Climate Hazard Infrared Group Precipitation Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). By combining the big data analytical potential of Google Earth Engine (GEE), we compare spatial patterns and temporal trends in observational and modelled precipitation and demonstrate that remote sensing products can reliably be used in inaccessible areas where observational data are scarce and/or temporally incomplete. CHIRPS reanalysis data indicate that there are in fact three significantly distinct annual rainfall periods in the basin, viz. phase 1: 1983 to 1997 (relatively high annual rainfall); phase 2: 1998 to 2008 (drought); phase 3: 2009 to 2018 (return to relatively high annual rainfall again). By comparison, PERSIANN-CDR data show reduced annual and winter precipitation, but no significant changes during the monsoon and pre-monsoon seasons from 1983 to 2008. The major conclusions of this study are that rainfall modelled using CHIRPS corresponds well with the observational record in confirming the decreased annual and seasonal rainfall, averaging 10.9 and 7.9 mm per decade respectively between 1983 and 2008, although there is a trend (albeit not statistically significant) to higher rainfall after the marked dry period between 1998 and 2008. Long-term variability in rainfall in the Bhilangana river basin has had critical impacts on the environment arising from water scarcity in this mountainous region.

Increasing demand for land resources at the coast has exerted immense pressure on vulnerable environments. Population and economic growth in coastal cities have combined to produce a scarcity of suitable space for development, the response to which has frequently been the reclamation of land from the sea, most prominently in China. Urbanization is a key driver of such changes and a detailed investigation of coastal land reclamation at the city scale is required. This study analyzed remote sensing imagery for the period 1990 to 2018 to explore the trajectories of coastal land reclamation in nine major urban agglomerations across the three largest deltas in China using the JRC Global Surface Water (Yearly Water Classification History, v1.1) (GSW) dataset on the Google Earth Engine platform. The results are considered in the context of major national policy reforms over the last three decades. The analysis reveals that total land reclaimed among nine selected cities had exceeded 2800 km² since 1984, 82% of which occurred after 2000, a year following the enactment of China’s agricultural ‘red line’ policy. Shanghai exhibited the greatest overall area of land extension, followed by Ningbo and Tianjin, especially in the period following the privatization of property rights in 2004. In analyzing annual trends, we identified the developmental stages of a typical coastal reclamation project and how these vary between cities. Scrutiny of the results revealed voids in nighttime light satellite data (2014–2018) in some localities. Although these voids appeared to be characterized by construction, they were occupied by vacant buildings, and were therefore examples of so-called “ghost cities.” In China, as elsewhere, continual land reclamation needs to be considered in relation to, inter alia, sea level rise and land subsidence that pose significant challenges to the vision of sustainable urban development in these three deltaic megacities.