Impaired lung function in individuals chronically exposed to biomass combustion

Background: The use of biomass for cooking and heating is considered an important factor associated with respiratory diseases. However, few studies evaluate the amount of particulate matter less than 2.5 mu in diameter (PM2.5), symptoms and lung function in the same population. Objectives: To evaluate the respiratory effects of biomass combustion and compare the results with those of individuals from the same community in Brazil using liquefied petroleum gas (Gas). Methods: 1402 individuals in 260 residences were divided into three groups according to exposure (Gas, Indoor-Biomass, Outside-Biomass). Respiratory symptoms were assessed using questionnaires. Reflectance of paper filters was used to assess particulate matter exposure. In 48 residences the amount of PM2.5 was also quantified. Pulmonary function tests were performed in 120 individuals. Results: Reflectance index correlated directly with PM2.5 (r=0.92) and was used to estimate exposure (ePM2.5). There was a significant increase in ePM2.5 in Indoor-Biomass and Outside-Biomass, compared to Gas. There was a significantly increased odds ratio (OR) for cough, wheezing and dyspnea in adults exposed to Indoor-Biomass (OR=2.93, 2.33, 2.59, respectively) and Outside-Biomass (OR=1.78, 1.78, 1.80, respectively) compared to Gas. Pulmonary function tests revealed both Non-Smoker-Biomass and Smoker-Gas individuals to have decreased %predicted-forced expiratory volume in the first second (FEV1) and FEV1/forced vital capacity (FVC) as compared to Non-Smoker-Gas. Pulmonary function tests data was inversely correlated with duration and ePM2.5. The prevalence of airway obstruction was 20% in both Non-Smoker-Biomass and Smoker-Gas subjects. Conclusion: Chronic exposure to biomass combustion is associated with increased prevalence of respiratory symptoms, reduced lung function and development of chronic obstructive pulmonary disease. These effects are associated with the duration and magnitude of exposure and are exacerbated by tobacco smoke. (C) 2011 Elsevier Inc. All rights reserved.

Calibration of a Species-Specific Spectral Vegetation Index for Leaf Area Index (LAI) Monitoring: Example with MODIS Reflectance Time-Series on Eucalyptus Plantations

The leaf area index (LAI) is a key characteristic of forest ecosystems. Estimations of LAI from satellite images generally rely on spectral vegetation indices (SVIs) or radiative transfer model (RTM) inversions. We have developed a new and precise method suitable for practical application, consisting of building a species-specific SVI that is best-suited to both sensor and vegetation characteristics. Such an SVI requires calibration on a large number of representative vegetation conditions. We developed a two-step approach: (1) estimation of LAI on a subset of satellite data through RTM inversion; and (2) the calibration of a vegetation index on these estimated LAI. We applied this methodology to Eucalyptus plantations which have highly variable LAI in time and space. Previous results showed that an RTM inversion of Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared and red reflectance allowed good retrieval performance (R-2 = 0.80, RMSE = 0.41), but was computationally difficult. Here, the RTM results were used to calibrate a dedicated vegetation index (called "EucVI") which gave similar LAI retrieval results but in a simpler way. The R-2 of the regression between measured and EucVI-simulated LAI values on a validation dataset was 0.68, and the RMSE was 0.49. The additional use of stand age and day of year in the SVI equation slightly increased the performance of the index (R-2 = 0.77 and RMSE = 0.41). This simple index opens the way to an easily applicable retrieval of Eucalyptus LAI from MODIS data, which could be used in an operational way.