Henrique Llacer Roig

Performance of CMORPH, TMPA, and PERSIANN rainfall datasets over plain, mountainous, and glacial regions of Pakistan

The present study aims at the assessment of six satellite rainfall estimates (SREs) in Pakistan. For each assessed products, both real-time (RT) and post adjusted (Adj) versions are considered to highlight their potential benefits in the rainfall estimation at annual, monthly, and daily temporal scales. Three geomorphological climatic zones, i.e., plain, mountainous, and glacial are taken under considerations for the determination of relative potentials of these SREs over Pakistan at global and regional scales. All SREs, in general, have well captured the annual north-south rainfall decreasing patterns and rainfall amounts over the typical arid regions of the country. Regarding the zonal approach, the performance of all SREs has remained good over mountainous region comparative to arid regions. This poor performance in accurate rainfall estimation of all the six SREs over arid regions has made their use questionable in these regions. Over glacier region, all SREs have highly overestimated the rainfall. One possible cause of this overestimation may be due to the low surface temperature and radiation absorption over snow and ice cover, resulting in their misidentification with rainy clouds as daily false alarm ratio has increased from mountainous to glacial regions. Among RT products, CMORPH-RT is the most biased product. The Bias was almost removed on CMORPH-Adj thanks to the gauge adjustment. On a general way, all Adj versions outperformed their respective RT versions at all considered temporal scales and have confirmed the positive effects of gauge adjustment. CMORPH-Adj and TMPA-Adj have shown the best agreement with in situ data in terms of Bias, RMSE, and CC over the entire study area.

A novel land use approach for assessment of human health: The relationship between urban structure types and cardiorespiratory disease risk.

Extensive evidence shows that in addition to lifestyle factors, environmental aspects are an important risk factor for human health. Numerous approaches have been used to estimate the relationship between environment and health. For example, the urban characteristics, especially the types of land use, are considered a potential proxy indicator to evaluate risk of disease. Although several studies have used land use variables to assess human health, none of them has used the concept of Urban Morphology by Urban Structure Types (USTs) as indicators of land use. The aim of this study was to assess the relationship between USTs and cardiorespiratory disease risks in the Federal District, Brazil. Toward this end, we used a quantile regression model to estimate risk. We used 21 types of UST. Income and population density were used as covariates in our sensitivity analysis. Our analysis showed an association between cardiorespiratory diseases risk and 10 UST variables (1 related to rural area, 6 related to residential area, 1 recreational area, 1 public area and 1 commercial area). Our findings suggest that the conventional land use method may be missing important information about the effect of land use on human health. The use of USTs can be an approach to complement the conventional method. This should be of interest to policy makers in order to enhance public health policies and to create future strategies in terms of urban planning, land use and environmental health.

Association between vehicular emissions and cardiorespiratory disease risk in Brazil and its variation by spatial clustering of socio-economic factors

Many studies have suggested that socio-economic factors are strong modifiers of human vulnerability to air pollution effects. Most of these studies were performed in developed countries, specifically in the US and Europe. Only a few studies have been performed in developing countries, and analyzed small regions (city level) with no spatial disaggregation. The aim of this study was to assess the association between vehicle emissions and cardiorespiratory disease risk in Brazil and its modification by spatial clustering of socio-economic conditions. We used a quantile regression model to estimate the risk and a geostatistical approach (K means) to execute spatial cluster analysis. We performed the risk analysis in three stages. First, we analyzed the entire study area (primary analysis), and then we conducted a spatial cluster analysis based on various municipal-level socio-economic factors, followed by a sensitivity analysis. We studied 5444 municipalities in Brazil between 2008 and 2012. Our findings showed a significant association between cardiorespiratory disease risk and vehicular emissions. We found that a 15% increase in air pollution is associated with a 6% increase in hospital admissions rates. The results from the spatial cluster analysis revealed two groups of municipalities with distinct sets of socio-economic factors and risk levels of cardiorespiratory disease related to exposure to vehicular emissions. For example, for vehicle emissions of PM in 2008, we found a relative risk of 4.18 (95% CI: 3.66, 4.93) in the primary analysis; in Group 1, the risk was 0.98 (95% CI: 0.10, 2.05) while in Group 2, the risk was 5.56 (95% CI: 4.46, 6.25). The risk in Group 2 was 480% higher than the risk in Group 1, and 35% higher than the risk in the primary analysis. Group 1 had higher values (3rd quartile) for urbanization rate, highway density, and GDP; very high values (≥3rd quartile) for population density; median values for distance from the capital; and lower values (1st quartile) for rural population density. Group 2 had lower values (1st quartile) urbanization rate; median values for highway density, GDP, and population density; between median and third quartile values for distance from the capital; and higher values (3rd quartile) for rural population density. Our findings suggest that socio-economic factors are important modifiers of the human risk of cardiorespiratory disease due to exposure to vehicle emissions in Brazil. Our study provides support for creating effective public policies related to environmental health that are targeted to high-risk populations.

Role of Climate Variability and Human Activity on Poopó Lake Droughts between 1990 and 2015 Assessed Using Remote Sensing Data

In 2015, an emergency state was declared in Bolivia when Poopo Lake dried up. Climate variability and the increasing need for water are potential factors responsible for this situation. Because field data are missing over the region, no statements are possible about the influence of mentioned factors. This study is a preliminary step toward the understanding of Poopo Lake drought using remote sensing data. First, atmospheric corrections for Landsat (FLAASH and L8SR), seven satellite derived indexes for extracting water bodies, MOD16 evapotranspiration, PERSIANN-CDR and MSWEP rainfall products potentiality were assessed. Then, the fluctuations of Poopo Lake extent over the last 26 years are presented for the first time jointly, with the mean regional annual rainfall. Three main droughts are highlighted between 1990 and 2015: two are associated with negative annual rainfall anomalies in 1994 and 1995 and one associated with positive annual rainfall anomaly in 2015. This suggests that other factors than rainfall influenced the recent disappearance of the lake. The regional evapotranspiration increased by 12.8% between 2000 and 2014. Evapotranspiration increase is not homogeneous over the watershed but limited over the main agriculture regions. Agriculture activity is one of the major factors contributing to the regional desertification and recent disappearance of Poopo Lake.

A spatial multicriteria model for determining air pollution at sample locations

Atmospheric pollution in urban centers has been one of the main causes of human illness related to the respiratory and circulatory system. Efficient monitoring of air quality is a source of information for environmental management and public health. This study investigates the spatial patterns of atmospheric pollution using a spatial multicriteria model that helps target locations for air pollution monitoring sites. The main objective was to identify high-priority areas for measuring human exposures to air pollutants as they relate to emission sources. The method proved to be viable and flexible in its application to various areas. Implications: Spatial multicriteria models provide a tool for air pollution management in urban areas. Analytic hierarchy process (AHP) modeling can help with the process of prioritizing monitoring site locations and minimizing costs.

Mapping distance-decay of cardiorespiratory disease risk related to neighborhood environments.

Neighborhood characteristics affect an individuals quality of life. Although several studies have examined the relationship between neighborhood environments and human health, we are unaware of studies that have examined the distance-decay of this effect and then presented the risk results spatially. Our study is unique in that is explores the health effects in a less developed country compared to most studies that have focused on developed countries. The objective of our study is to quantify the distance-decay cardiorespiratory diseases risk related to 28 neighborhood aspects in the Federal District, Brazil and present this information spatially through risk maps of the region. Toward this end, we used a quantile regression model to estimate risk and GIS modeling techniques to create risk maps. Our analysis produced the following findings: i) a 2500 m increase in highway length was associated with a 46% increase in cardiorespiratory diseases; ii) 46,000 light vehicles in circulation (considering a buffer of ≤500 m from residences) was associated with 6 hospital admissions (95% CI: 2.6, 14.6) per cardiorespiratory diseases; iii) 74,000 m2 of commercial areas (buffer ≤1700 m) was associated with 12 hospital admissions (95% CI: 2.2, 20.8); iv) 1km2 increase in green areas intra urban was associated with less two hospital admissions, and; vi) those who live ≤500 m from the nearest point of wildfire are more likely to have cardiorespiratory diseases that those living >500 m. Our findings suggest that the approach used in this study can be an option to improve the public health policies.

Spatiotemporal analysis of traffic emissions in over 5000 municipal districts in Brazil

ABSTRACT Exposure to traffic emission is harmful to human health. Emission inventories are essential to public health policies aiming at protecting human health, especially in areas with incomplete or nonexistent air pollution monitoring networks. In Brazil, for example, only 1.7% of municipal districts have a monitoring network, and only a few studies have reported data on vehicle emission inventories. No studies have presented emission inventories by municipality. In this study, we predicted vehicular emissions for 5570 municipal districts in Brazil during the period 2001–2012. We used a top-down method to estimate emissions. Carbon dioxide (CO2) is the pollutant with the highest emissions, with approximately 190 million tons per year during the period 2001–2012). For the other traffic-related pollutants, we predicted annual emissions of 1.5 million tons for carbon monoxide (CO), 1.2 million tons of nitrogen oxides (NOx), 209,000 tons of nonmethane hydrocarbons (NMHC), 58,000 tons of particulate matter (PM), and 42,000 tons for methane (CH4). From 2001 to 2012, CO, NMHC, and PM emissions decreased by 41, 33, and 47%, respectively, whereas those CH4, NOx, and CO2 increased by 2, 4, and 84%, respectively. We estimated uncertainties in our study and found that NOx was the pollutant with the lowest percentage difference, 8%, and NMHC with the highest one, 30%. For CO, CH4, CO2, and PM, the values were 22, 14, 21, and 20%, respectively. Finally, we found that during 2001 and 2012 emissions increased in the Northwest and Northeast. In contrast, pollutant emissions, except for CO2, decreased in the Southeast, South, and part of Midwest. Our predictions can be critical to efforts developing cost-effective public policies tailored to individual municipal districts in Brazil. Implications: Emission inventories may be an alternative approach to provide data for air quality forecasting in areas where air quality data are not available. This approach can be an effective tool in developing spatially resolved emission inventories.

ANÁLISE E MAPEAMENTO DO USO E OCUPAÇÃO DO SOLO DA BACIA DO ALTO DO DESCOBERTO, DF/GO, POR MEIO DE CLASSIFICAÇÃO AUTOMÁTICA BASEADA EM REGRAS E LÓGICA NEBULOSA

The Alto Descoberto basin is an important water source for the Distrito Federal. During the past years, this basin has suffered an agriculture and urban expansion over the natural areas. The inadequate land use associated with a lack of planning the use and occupation of the land, can lead to alteration in the superficial water flow and hydrological cycle, to siltation, to pollution of the superficial and underground water bodies and also towards an unbalanced environment in the region. Because of that, the monitoring and analysis of land use and occupation are extremely needed in the prevention of these impacts and maintenance of the water bodies. Keeping that in mind, this study aimed to perform the mapping and comparative analysis of the land use and occupation of the soil of the Alto do Descoberto basin between 1994 and 2011. The classifications of use and occupation of the soil were elaborated using a classifier oriented to the fuzzy rulebased object, allowing the analysis of the advances of the soil use and loss of vegetal coverage of the basin. The classification system was considered successful, with thematic accuracy (Kappa index) of 0.64. Although there are still some thematic confusions during the classification process, the comparative analysis among theme classes of the maps of the referred years showed a strong agriculture expansion of the region in approximately 11% of the river basin area, as well as a loss of the natural vegetation.

CHALLENGES AND STRATEGIES ON IMPLEMENTING AN ECOLOGICAL CORRIDOR BETWEEN PROTECTED AREAS IN CERRADO BIOME

Medidas que visem a reconectar a paisagem, como corredores ecologicos, sao de suma importância para reverter os impactos da degradacao e fragmentacao de habitats sobre a biodiversidade. Este trabalho propoe uma metodologia que utiliza ferramentas de geoprocessamento para definicao de corredores ecologicos. O estudo de caso foi realizado no limite estadual do Distrito Federal e Goias com intuito de propor um corredor ecologico para mamiferos de medio e grande entre a Estacao Ecologica de Aguas Emendadas e o Campo de Instrucao de Formosa. As analises de custo realizadas utilizando dados oficiais gratuitos resultaram em tres trajetos (A, B, C), com extensoes variando de 28,88 (A) a 47,34 km (C). A sobreposicao dos trajetos com remanescentes de vegetacao natural variou de 74,43% (A) a 90,35% (B) e a interseccao com APP potenciais de 66,32% (A) a 86,26% (C). A sobreposicao com propriedades rurais cadastradas no CAR (Cadastro Ambiental Rural) foi analisada para os melhores caminhos A e B, revelando 51,60% e 50,97% de sobreposicao, respectivamente, indicando areas predispostas para o processo de regularizacao ambiental. O uso das ferramentas de analise de custo aliado ao metodo AHP mostrou-se eficiente para delimitacao de corredores ecologicos.

Multisource data for seasonal variability analysis of cyanobacteria in a tropical inland aquatic environment

Cyanobacterial blooms are related to eutrophic conditions that compromise the many uses of reservoirs. Thus, quick and effective methods for detecting the abundance of cyanobacteria in waterbodies are needed to complement conventional laboratory methods. In addition, inadequate control techniques that are applied at times of high cyanobacterial concentrations can cause the cells to lyse and release toxins into the water. In the present study we investigated the behaviour of cyanobacteria by determining phycocyanin and chlorophyll concentrations, using spectroradiometric and fluorometric techniques, in three field campaigns performed at the Nova Avanhandava Reservoir, Brazil. The sampling rate and favourable season for data collected had been determined previously by remote sensing analysis. Seasonal estimates of cyanobacteria were made because fluorometric sensors were able to record low concentrations, whereas the spectral analyses only detected phycocyanin at higher concentrations. Results of spectral analyses highlighted the subtle spectral characteristics indicating the presence of phycocyanin, even without a clear definition of the diagnostic features in the reflectance curve. Therefore, multiscale remote sensing complemented by fluorometric analysis and relevant environmental variables is an effective approach for monitoring cyanobacteria in Brazilian inland waters.