Air pollution is a major risk factor for global health, with 3 million deaths annually being attributed to fine particulate matter ambient pollution (PM2.5). The primary source of information for estimating population exposures to air pollution has been measurements from ground monitoring networks but, although coverage is increasing, regions remain in which monitoring is limited. The data integration model for air quality supplements ground monitoring data with information from other sources, such as satellite retrievals of aerosol optical depth and chemical transport models.

As is true in many regions, India experiences surface Urban Heat Island (UHI) effect that is well understood, but the causes of the more recently discovered Urban Cool Island (UCI) effect remain poorly constrained. This raises questions about our fundamental understanding of the drivers of rural-urban environmental gradients and hinders development of effective strategies for mitigation and adaptation to projected heat stress increases in rapidly urbanizing India. Here we show that more than 60% of Indian urban areas are observed to experience a day-time UCI.

Changes in the abundance and area of mountain lakes in the Djungarskiy (Jetysu) Alatau between 2002 and 2014 were investigated using Landsat imagery. The number of lakes increased by 6.2 % from 599 to 636 with a growth rate of 0.51 % a−1. The combined areas were 16.26 ± 0.85 to 17.35 ± 0.92 km2 respectively and the overall change was within the uncertainty of measurements. Fifty lakes, whose potential outburst can damage existing infrastructure, were identified.

The rock avalanche that destroyed the village of Xinmo in Sichuan, China, on June 24th, 2017, brought the issue of landslide risk and disaster chain management in highly seismic regions back into the spotlight. The long-term post-seismic behaviour of mountain slopes is complex and hardly predictable. Nevertheless, the integrated use of field monitoring, remote sensing and real-time predictive modelling can help to set-up effective early warning systems, provide timely alarms, optimize rescue operations and perform secondary hazard assessments.

Changes in tropical wetland, ruminant or rice emissions are thought to have played a role in recent variations in atmospheric methane (CH4) concentrations. India has the world’s largest ruminant population and produces ~ 20% of the world’s rice. Therefore, changes in these sources could have significant implications for global warming. Here, we infer India’s CH4 emissions for the period 2010–2015 using a combination of satellite, surface and aircraft data.

Original Source

This study illustrates the development and implementation of a novel rapid response storm impact survey that combined the use of drone based aerial photogrammetry with numerical modeling.  The comprehensive approach employed in this pilot case study was conducted on the Emilia-Romagna coast (Italy), in the immediate aftermath of an extreme storm event that impacted the shoreline on the 5th-6th February 2015 called the “Saint Agatha Storm”.

In many coastal communities, the risks driven by storm surges are motivating substantial investments in flood risk management. The design of adaptive risk management strategies, however, hinges on the ability to detect future changes in storm surge statistics. Previous studies have used observations to identify changes in past storm surge statistics. Here, we focus on the simple and decision-relevant question: How fast can we learn from past and potential future storm surge observations about changes in future statistics?

In response to global warming, the Brewer–Dobson circulation in the stratosphere is expected to accelerate and the mean transport time of air along this circulation to decrease. This would imply a negative stratospheric age of air trend, i.e. an air parcel would need less time to travel from the tropopause to any point in the stratosphere. Age of air as inferred from tracer observations, however, shows zero to positive trends in the northern mid-latitude stratosphere and zonally asymmetric patterns.