Agricultural Fires and Air Quality Degradation in Northwestern India

In northwestern India, the double crop system alternates between the monsoon crop (kharif, predominatly rice) and winter crop (rabi, predominatly wheat). The short turnaround between harvest and sowing pressures farmers to quickly remove crop residue from the field to prepare for the next planting. In addition, the use of combine harvesters, which saves time and decreases labor costs, leaves behind abundant root-bound residues that are difficult to remove manually. The prevailing, cost-effective method is to burn the crop residue. However, crop residue burning releases a suite of pernicious gases and aerosols into the atmosphere that degrades both rural and urban air quality. Although crop residue burning is just one of many pollution sources, it is highly episodic and seasonal. Every year, the burning of rice residue from October to November contributes to thick haze over the region. New Delhi, a mega-city already suffering from heavy local pollution, is located downwind of these seasonal agricultural fires. During the post-monsoon, meterology (e.g. slow winds and colder temperatures) help trap pollutants near the surface in north India, where flat terrain is surrounded by the Himalayas. We first used HYSPLIT atmospheric back trajectories to define Delhi's post-monsoon (October-November) and pre-monsoon (April-May) airsheds, or approximate regions where emissions can affect Delhi's local air pollution (Liu et al., 2018a). Focusing on the post-monsoon burning season, we then used the STILT model to estimate the PM_2.5 enhancement in the Delhi National Capital Territory (Cusworth et al., 2018). We also developed a fusion method using MODIS (500 m x 500 m) and Landsat (30 m x 30 m) imagery to approximate burned area; however, we find that moderate-resolution sensors, such as MODIS, are unable to see many small fires, therefore underestimating overall fire activity (Liu et al., 2019). In future work, we will build an agricultural fire emissions inventory to investigate the transport and public health impact of these emissions.

Publications: Liu et al. (2018a, Atmos. Environ.), Cusworth et al. (2018, Environ. Res. Lett.), Liu et al. (2019, Environ. Res. Commun.)

Primary Collaborators: Miriam Marlier (RAND Corporation), Ruth DeFries (Columbia), Alex Karambelas (Columbia) Dan Cusworth (Harvard), Loretta Mickley (Harvard), Ritesh Gautam (EDF), Manoj Singh (UPES), Meha Jain (U Michigan)

Pre-burn (Oct 12, 2014) and post-burn (Oct 20, 2014) in western Punjab, India (DigitalGlobe, Google Earth)

Modeling the Public Health Impacts of Fires in Equatorial Asia

In strong El Niño years (ver dry conditions), such as 2006 and 2015, fires associated with oil palm, timber, and logging plantations and deforestation in Indonesia lead to severe, persistent haze over Equatorial Asia from July to November. In part due to drought conditions associated with El Niño and fires on peat, or partially decayed organic material, fires were able to persist and associated emissions exceeded yearly U.S. fossil fuel emissions on many days during the 2015 fire season (WRI, GFED). Peat sequesters carbon in meters-deep layers accumulated over thousands of years; peat fires, then, reduce the ability of Indonesia to be a carbon sink. In past work, we developed a framework to rapidly estimate the public health impacts of the 2015 severe haze episode in Equatorial Asia. Overall, smoke exposure, weighted by population, more than doubled from the 2006 to 2015 severe haze events. Based on chemical transport modeling, fire emissions in the provinces of Jambi and South Sumatra and West and Central Kalimantan contributed to the bulk of population-weighted smoke exposure in Indonesia, Singapore, and Malaysia (Koplitz et al., 2016). We then used Google Earth Engine to build the SMOKE public policy tool, which estimates the public health benefits of reducing fires in various land use scenarios, including peatland restoration. In more recent work, we find that using different global fire emissions inventories can significanly impact modeled estimates of smoke particulate matter, particularly in Equatorial Asia (Marlier et al., 2019). To help end-users quickly compare five widely-used inventories and diagnose their underlying assumptions, we developed the FIRECAM tool (Liu et al., in review).

Publications: Koplitz et al. (2016, Environ. Res. Lett.), Marlier et al. (2019, GeoHealth), Liu et al. (in review, Remote Sens. Environ.)

Primary Collaborators: Miriam Marlier (RAND Corporation), Ruth DeFries (Columbia), Shannon Koplitz (EPA), Loretta Mickley (Harvard), Daniel Jacob (Harvard), Sam Myers (Harvard), Jonathan Buonocore (Harvard), Joel Schwartz (Harvard)

Population-weighted smoke exposure in Singapore, Indonesia, and Malaysia by contributing province (Koplitz et al., 2016)

Global Search for Sea Surface Salinity Predictors of Regional Rainfall Patterns

The global ocean makes up the bulk of the global water cycle, and is the ultimate source of all rainfall, implying that changes in the ocean affect rainfall patterns on land. Traditionally, seasonal to decadal sea surface temperature (SST)‐based patterns have been linked to variations in rainfall over land. However, the salinity of the surface ocean is directly responsive to changes in evaporation and precipitation. We find that abnormally salty (more evaporation) or fresh (more precipitation) patches of the ocean can be used to predict rainfall on land one season ahead. Specifically, we looked globally for changes in autumn sea surface salinity (SSS) that correspond well with variations in winter precipitation in the southwestern United States to build a SSS‐based model. We find that the SSS‐based model outperforms the SST‐based model. Thus, incorporating SSS into existing frameworks for predicting seasonal rainfall on land can improve forecasts needed for allocating water resources ahead of abnormally dry or wet seasons.

Publication: Liu et al. (2018b, Geophys. Res. Lett.)

Primary Collaborators: Ray Schmitt (WHOI), Laifang Li (Duke)

Correlation of autumn sea surface salinity with winter precipitation in southwestern United States and identified SSS predictors (Liu et al., 2018)