Transport related air pollution and its implications on public health along selected road corridors in lagos metropolis, nigeria

The study investigated the ambient air quality caused by vehicular emission and its implications on the public health around major roadways in Lagos metropolis Nigeria. Field data on vehicular volumes and mix were collected for three months in the morning, afternoon, and evening peak periods for the five (5) selected major routes. Concurrently, air pollutants from vehicles were measured by portable gas detectors on the routes. Questionnaires were administered to the respondent near the routes to investigate the implications of exposure on their health. The concentration level of the air pollutants is highest between 8-9 am morning peak periods and lowest between 12-1 pm afternoon periods. The ambient air quality is polluted on all the studied routes and revealed a strong correlation (p<0.05) between pollutants concentration and traffic flow. The questionnaire results also showed that 74% of the sampled respondents around the corridor suffered from chest pain, frequent cough, nose running and sneezing, sore throat, difficulty in breathing, body weakness, fatigue, eye irritation, loss of appetite, headache, and fast breathing of which 6% of children and 54% of women were the most susceptible. The study recommended measures for the reduction of the negative impacts on ambient air quality and public health in developing African citiesPapers presented at the 40th International Southern African Transport Conference on 04 -08 July 202

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia

© 2015 Elsevier Inc. This study explores the benefits of assimilating SMOS soil moisture retrievals for hydrologic modeling, with a focus on soil moisture and streamflow simulations in the Murray Darling Basin, Australia. In this basin, floods occur relatively frequently and initial catchment storage is known to be key to runoff generation. The land surface model is the Variable Infiltration Capacity (VIC) model. The model is calibrated using the available streamflow records of 169 gauge stations across the Murray Darling Basin. The VIC soil moisture forecast is sequentially updated with observations from the SMOS Level 3 CATDS (Centre Aval de Traitement des Données SMOS) soil moisture product using the Ensemble Kalman filter. The assimilation algorithm accounts for the spatial mismatch between the model (0.125°) and the SMOS observation (25km) grids. Three widely-used methods for removing bias between model simulations and satellite observations of soil moisture are evaluated. These methods match the first, second and higher order moments of the soil moisture distributions, respectively. In this study, the first order bias correction, i.e. the rescaling of the long term mean, is the recommended method. Preserving the observational variability of the SMOS soil moisture data leads to improved soil moisture updates, particularly for dry and wet conditions, and enhances initial conditions for runoff generation. Second or higher order bias correction, which includes a rescaling of the variance, decreases the temporal variability of the assimilation results. In comparison with in situ measurements of OzNet, the assimilation with mean bias correction reduces the root mean square error (RMSE) of the modeled soil moisture from 0.058m3/m3 to 0.046m3/m3 and increases the correlation from 0.564 to 0.714. These improvements in antecedent wetness conditions further translate into improved predictions of associated water fluxes, particularly runoff peaks. In conclusion, the results of this study clearly demonstrate the merit of SMOS data assimilation for soil moisture and streamflow predictions at the large scale.publisher: Elsevier articletitle: SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia journaltitle: Remote Sensing of Environment articlelink: http://dx.doi.org/10.1016/j.rse.2015.06.025 content_type: article copyright: Copyright © 2015 Elsevier Inc. All rights reserved.status: publishe

Assimilation of SMOS soil moisture and brightness temperature products into a land surface model

© 2015 Elsevier Inc. The Soil Moisture and Ocean Salinity (SMOS) mission has the potential to improve the predictive skill of land surface models through the assimilation of its observations. Several alternate products can be distinguished: the observed brightness temperature (TB) data at coarse scale, indirect estimates of soil moisture (SM) through the inversion of the coarse-scale TB observations, and fine-scale soil moisture through the a priori downscaling of coarse-scale soil moisture. The SMOS TB products include observations over a large range of incidence angles at both H- and V-polarizations, which allows the merit of assimilating the full set of multi-angular/polarization observations, as opposed to specific sub-sets of observations, to be assessed. This study investigates the performance of various observation scenarios with respect to soil moisture and streamflow predictions in the Murray Darling Basin. The observations are assimilated into the Variable Infiltration Capacity (VIC) model, coupled to the Community Microwave Emission Modeling (CMEM) platform, using the Ensemble Kalman filter. The assimilation of these various observation products is assessed under similar realistic assimilation settings, without optimization, and validated by comparison of the modeled soil moisture and streamflow to in situ measurements across the basin. The best results are achieved from assimilation of the coarse-scale SM observations. The reduced improvement using downscaled SM is probably due to a lower number of observations, as a result of cloud cover effects on the downscaling method. The assimilation of TB was found to be a promising alternative, which led to improvements in soil moisture prediction approaching those of the coarse-scale SM assimilation.publisher: Elsevier articletitle: Assimilation of SMOS soil moisture and brightness temperature products into a land surface model journaltitle: Remote Sensing of Environment articlelink: http://dx.doi.org/10.1016/j.rse.2015.10.033 content_type: article copyright: © 2015 Elsevier Inc. All rights reserved.status: publishe

SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia

This study explores the benefits of assimilating SMOS soil moisture retrievals for hydrologic modeling, with a focus on soil moisture and streamflow simulations in the Murray Darling Basin, Australia. In this basin, floods occur relatively frequently and initial catchment storage is known to be key to runoff generation. The land surface model is the Variable Infiltration Capacity (VIC) model. The model is calibrated using the available streamflow records of 169 gauge stations across the Murray Darling Basin. The VIC soil moisture forecast is sequentially updated with observations from the SMOS Level 3 CATDS (Centre Aval de Traitement des Données SMOS) soil moisture product using the Ensemble Kalman filter. The assimilation algorithm accounts for the spatial mismatch between the model (0.125°) and the SMOS observation (25 km) grids. Three widely-used methods for removing bias between model simulations and satellite observations of soil moisture are evaluated. These methods match the first, second and higher order moments of the soil moisture distributions, respectively. In this study, the first order bias correction, i.e. the rescaling of the long term mean, is the recommended method. Preserving the observational variability of the SMOS soil moisture data leads to improved soil moisture updates, particularly for dry and wet conditions, and enhances initial conditions for runoff generation. Second or higher order bias correction, which includes a rescaling of the variance, decreases the temporal variability of the assimilation results. In comparison with in situ measurements of OzNet, the assimilation with mean bias correction reduces the root mean square error (RMSE) of the modeled soil moisture from 0.058 m3/m3 to 0.046 m3/m3 and increases the correlation from 0.564 to 0.714. These improvements in antecedent wetness conditions further translate into improved predictions of associated water fluxes, particularly runoff peaks. In conclusion, the results of this study clearly demonstrate the merit of SMOS data assimilation for soil moisture and streamflow predictions at the large scale

Assimilation of SMOS soil moisture and brightness temperature products into a land surface model

The Soil Moisture and Ocean Salinity (SMOS) mission has the potential to improve the predictive skill of land surface models through the assimilation of its observations. Several alternate products can be distinguished: the observed brightness temperature (TB) data at coarse scale, indirect estimates of soil moisture (SM) through the inversion of the coarse-scale TB observations, and fine-scale soil moisture through the a priori downscaling of coarse-scale soil moisture. The SMOS TB products include observations over a large range of incidence angles at both H- and V-polarizations, which allows the merit of assimilating the full set of multi-angular/polarization observations, as opposed to specific sub-sets of observations, to be assessed. This study investigates the performance of various observation scenarios with respect to soil moisture and streamflow predictions in the Murray Darling Basin. The observations are assimilated into the Variable Infiltration Capacity (VIC) model, coupled to the Community Microwave Emission Modeling (CMEM) platform, using the Ensemble Kalman filter. The assimilation of these various observation products is assessed under similar realistic assimilation settings, without optimization, and validated by comparison of the modeled soil moisture and streamflow to in situ measurements across the basin. The best results are achieved from assimilation of the coarse-scale SM observations. The reduced improvement using downscaled SM is probably due to a lower number of observations, as a result of cloud cover effects on the downscaling method. The assimilation of TB was found to be a promising alternative, which led to improvements in soil moisture prediction approaching those of the coarse-scale SM assimilatio

Optimization of a Radiative Transfer Forward Operator for Simulating SMOS Brightness Temperatures over the Upper Mississippi Basin

The Soil Moisture Ocean Salinity (SMOS) satellite mission routinely provides global multiangular observations of brightness temperature TB at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture SM. To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multiangular and multipolarization top of the atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity model coupled with the Community Microwave Emission Modelling Platform for simulating SMOS TB observations over the upper Mississippi basin, United States. For a period of 2 years (2010–11), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin-averaged bias of 30 K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After cumulative distribution function matching the SM climatology of the LSM to SMOS retrievals, the average bias decreases from 30 K to less than 5 K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation