Assessing costs of Indonesian fires and the benefits of restoring peatland.

Deforestation and drainage has made Indonesian peatlands susceptible to burning. Large fires occur regularly, destroying agricultural crops and forest, emitting large amounts of CO2 and air pollutants, resulting in adverse health effects. In order to reduce fire, the Indonesian government has committed to restore 2.49 Mha of degraded peatland, with an estimated cost of US$3.2-7 billion. Here we combine fire emissions and land cover data to estimate the 2015 fires, the largest in recent years, resulted in economic losses totalling US$28 billion, whilst the six largest fire events between 2004 and 2015 caused a total of US$93.9 billion in economic losses. We estimate that if restoration had already been completed, the area burned in 2015 would have been reduced by 6%, reducing CO2 emissions by 18%, and PM2.5 emissions by 24%, preventing 12,000 premature mortalities. Peatland restoration could have resulted in economic savings of US$8.4 billion for 2004-2015, making it a cost-effective strategy for reducing the impacts of peatland fires to the environment, climate and human health.

Comparison of Models Analyzing a Small Number of Observed Meningitis Cases in Navrongo, Ghana.

The "meningitis belt" is a region in sub-Saharan Africa where annual outbreaks of meningitis occur, with epidemics observed cyclically. While we know that meningitis is heavily dependent on seasonal trends, the exact pathways for contracting the disease are not fully understood and warrant further investigation. Most previous approaches have used large sample inference to assess impacts of weather on meningitis rates. However, in the case of rare events, the validity of such assumptions is uncertain. This work examines the meningitis trends in the context of rare events, with the specific objective of quantifying the underlying seasonal patterns in meningitis rates. We compare three main classes of models: the Poisson generalized linear model, the Poisson generalized additive model, and a Bayesian hazard model extended to accommodate count data and a changing at-risk population. We compare the accuracy and robustness of the models through the bias, RMSE, and standard deviation of the estimators, and also provide a detailed case study of meningitis patterns for data collected in Navrongo, Ghana.

Population exposure to hazardous air quality due to the 2015 fires in Equatorial Asia.

Vegetation and peatland fires cause poor air quality and thousands of premature deaths across densely populated regions in Equatorial Asia. Strong El-Niño and positive Indian Ocean Dipole conditions are associated with an increase in the frequency and intensity of wildfires in Indonesia and Borneo, enhancing population exposure to hazardous concentrations of smoke and air pollutants. Here we investigate the impact on air quality and population exposure of wildfires in Equatorial Asia during Fall 2015, which were the largest over the past two decades. We performed high-resolution simulations using the Weather Research and Forecasting model with Chemistry based on a new fire emission product. The model captures the spatio-temporal variability of extreme pollution episodes relative to space- and ground-based observations and allows for identification of pollution sources and transport over Equatorial Asia. We calculate that high particulate matter concentrations from fires during Fall 2015 were responsible for persistent exposure of 69 million people to unhealthy air quality conditions. Short-term exposure to this pollution may have caused 11,880 (6,153-17,270) excess mortalities. Results from this research provide decision-relevant information to policy makers regarding the impact of land use changes and human driven deforestation on fire frequency and population exposure to degraded air quality.

Contrast and correlations between coarse and fine particulate matter in the United States.

The characteristics of concentrations of PM10₋2.5, PM2.5, and PM10 at 77 sites in the United States are evaluated. PM10 concentrations show strong spatial variability, with highest levels occurring in the southwestern United States, driven primarily by PM10₋2.5. PM10₋2.5 and PM2.5 concentrations show different spatial patterns. The highest concentrations of PM10₋2.5 were observed at sites in the southwestern US, leading to the highest PM10 concentrations there. The PM2.5 concentrations are the major contributors to the average PM10 concentrations at many sites in the eastern United States. Poor correlations were generally found between PM10₋2.5 and PM2.5, suggesting that PM10₋2.5 and PM2.5 are generally influenced by different sources. PM10₋2.5 is generally more variable than PM2.5 because PM10₋2.5 has a higher deposition velocity and is primarily emitted from mechanical processes (e.g. agricultural harvest and construction) that are more influenced by factors including human operation and wind speed leading to a strong episodic nature. As a result of its high variability, PM10₋2.5 acts as the major driver for PM10 extremes. PM10₋2.5 is significantly correlated with PM10 at all investigated sites, with the average correlation value R(2)=0.79. Correlations of PM2.5 with PM10 (average of 0.37) are overall considerably lower than those between PM10₋2.5 and PM10. Different seasonal, weekly, and diurnal patterns were observed between PM10₋2.5 and PM2.5 at agricultural, on-road traffic, quarrying, airport, and marine sites. At investigated agricultural sites, while the concentrations of PM2.5 are higher in winter when there are few agricultural activities, PM10₋2.5 concentrations are lower in winter months than in summer and autumn months, with highest levels corresponding to harvest and planting. The harvest and planting signatures were not observed in PM2.5 concentrations at any of these sites, suggesting that agricultural activities do not have a strong influence on PM2.5 concentrations.

Impact of trash burning on air quality in Mexico City.

Air pollution experienced by expanding urban areas is responsible for serious health effects and death for millions of people every year. Trash burning is a common disposal method in poor areas, yet it is uncontrolled in many countries, and its contribution to air pollution is unclear due to uncertainties in its emissions. Here we develop a new trash burning emission inventory for Mexico City based on inverse socioeconomic levels and recently measured emission factors, and apply a chemistry-transport model to analyze the effects on pollutant concentrations. Trash burning is estimated to emit 25 tons of primary organic aerosols (POA) per day, which is comparable to fossil fuel POA emissions in Mexico City, and causes an increase in average organic aerosol concentrations of ∼0.3 µg m(-3) downtown and up to 2 µg m(-3) in highly populated suburbs near the sources of emission. An evaluation using submicrometer antimony suggests that our emission estimates are reasonable. Mitigation of trash burning could reduce the levels of organic aerosols by 2-40% and those of PM(2.5) by 1-15% over the metropolitan area. The trash burning contributions to carbon monoxide, nitrogen oxides, and volatile organic compounds were found to be very small (<3%), and consequently the contributions to secondary nitrate, sulfate, and secondary organic aerosols are also very small.

Effects of land use data on dry deposition in a regional photochemical model for eastern Texas.

Land use data are among the inputs used to determine dry deposition velocities for photochemical grid models such as the Comprehensive Air Quality Model with extensions (CAMx) that is currently used for attainment demonstrations and air quality planning by the state of Texas. The sensitivity of dry deposition and O3 mixing ratios to land use classification was investigated by comparing predictions based on default U.S. Geological Survey (USGS) land use data to predictions based on recently compiled land use data that were collected to improve biogenic emissions estimates. Dry deposition of O3 decreased throughout much of eastern Texas, especially in urban areas, with the new land use data. Predicted 1-hr averaged O3 mixing ratios with the new land use data were as much as 11 ppbv greater and 6 ppbv less than predictions based on USGS land use data during the late afternoon. In addition, the area with peak O3 mixing ratios in excess of 100 ppbv increased significantly in urban areas when deposition velocities were calculated based on the new land use data. Finally, more detailed data on land use within urban areas resulted in peak changes in O3 mixing ratios of approximately 2 ppbv. These results indicate the importance of establishing accurate, internally consistent land use data for photochemical modeling in urban areas in Texas. They also indicate the need for field validation of deposition rates in areas experiencing changing land use patterns, such as during urban reforestation programs or residential and commercial development.