Background Indonesian peatlands have been drained for agricultural development for several decades. This development has made a major contribution to economic development. At the same time, peatland drainage is causing significant air pollution resulting from peatland fires. Peatland fires occur every year, even though their extent is much larger in dry (El Niño) years. We examine the health effects of long-term exposure to fine particles (PM2.5) from all types of peatland fires (including the burning of above and below ground biomass) in Sumatra and Kalimantan, where most peatland fires in Indonesia take place. Methods We derive PM2.5 concentrations from satellite imagery calibrated and validated with Indonesian Government data on air pollution, and link increases in these concentrations to peatland fires, as observed in satellite imagery. Subsequently, we apply available epidemiological studies to relate PM2.5 exposure to a range of health outcomes. The model utilizes the age distribution and disease prevalence of the impacted population. Results We find that PM2.5 air pollution from peatland fires, causes, on average, around 33,100 adults and 2900 infants to die prematurely each year from air pollution. In addition, peatland fires cause on average around 4390 additional hospitalizations related to respiratory diseases, 635,000 severe cases of asthma in children, and 8.9 million lost workdays. The majority of these impacts occur in Sumatra because of its much higher population density compared to Kalimantan. A main source of uncertainty is in the Concentration Response Functions (CRFs) that we use, with different CRFs leading to annual premature adult mortality ranging from 19,900 to 64,800 deaths. Currently, the population of both regions is relatively young. With aging of the population over time, vulnerabilities to air pollution and health effects from peatland fires will increase. Conclusions Peatland fire health impacts provide a further argument to combat fires in peatlands, and gradually transition to peatland management models that do not require drainage and are therefore not prone to fire risks.

Artificial neural network (ANN) is widely used for modelling in environmental science including climate, especially in rainfall prediction. Current knowledge has used several predictors consisting of historical rainfall data and El Niño Southern Oscillation (ENSO). However, rainfall variability of Indonesian is not only driven by ENSO, but Indian Ocean Dipole (IOD) could also influence variability of rainfall. Here, we proposed to use Dipole Mode Index (DMI) as index of IOD as complementary for ENSO. We found that rainfall variability in region with a monsoonal pattern has a strong correlation with ENSO and DMI. This strong correlation occurred during June-November, but a weak correlation was found for region with rainfall’s equatorial pattern. Based on statistical criteria, our model has R² 0.59 to 0.82, and RMSE 0.04-0.09 for monsoonal region. This finding revealed that our model is suitable to be applied in monsoonal region. In addition, ANN based model likely shows a low accuracy when it uses for long period prediction.

Jambi covers various land uses with different characteristics related to biogeophysical cycle. Land use plays an important role in the atmosphere-surface interaction and energy balance partition, which influenced rainfall pattern. Two proxies widely used to differentiate various land uses are albedo and normalized difference vegetation index (NDVI). However, study on albedo and NDVI relationship with rainfall in Jambi is still limited. This study aims to analyze the correlation of NDVI and albedo with rainfall and their distribution in Jambi and Muaro Jambi in 2013 and 2017. The research used Landsat 8 OLI TIRS satellite image data to derived NDVI and albedo, and CHIRPS data for rainfall. A simple linear regression was used to calculate the correlation of NDVI and albedo with rainfall. The results showed that the distribution of albedo for each land use class from the lowest to the highest was forest, plantation, cropland, shrubs, and settlements, respectively. On the contrary, the distribution of NDVI and rainfall is the inverse to albedo. Albedo and NDVI had a strong influence on rainfall through surface energy balance partition. This was indicated by the high R-square between albedo and rainfall (0.99) and between NDVI and rainfall (0.97). Increasing upward latent heat flux from the land surface to atmosphere leads to a rainfall increase. In other words, rainfall may also increase with the decrease in albedo, increase in NDVI, or land use change.

Indonesian peat ecosystem, generally managed for protection and cultivation functions, contributes to providing economic benefits to the local community through agricultural practices. This study aims to evaluate the feasibility of local commodity agriculture (coffee, areca, coconut, and pineapple) on peatland from social, ecological, and economic perspectives using descriptive and quantitative approaches in Mendahara-Batanghari, Jambi. Data was collected from interviews of 60 farmers in two villages with three types of farms, including monoculture and polyculture of commodities. The results of this study showed that socially, farmers still have difficulty with access and infrastructures. Coconut, areca, and coffee are popular among the smallholders because of land suitability, low maintenance, and high selling price. From an ecological perspective, intercropping on polyculture farms is able to store more carbon with a high density of biomass than on monoculture farms. Generally, the carbon emissions of local commodity farms are lower than other types of plantations, such as oil palm and rubber plantations. Lastly, from an economic perspective, local commodity farming in the study area is feasible based on analyses of net present value, benefit cost ratio, and internal rate of return. Polyculture farms provide higher benefits compared to monoculture land. The income is considered sufficient for standard living needs, and there is potential to increase the revenue by developing and optimising processing product industries.