Developing brownfields ranking models using decision analytic methods

The redevelopment of Brownfields has taken off in the 1990s, supported by federal and state incentives, and largely accomplished by local initiatives. Brownfields redevelopment has several associated benefits. These include the revitalization of inner-city neighborhoods, creation of jobs, stimulation of tax revenues, greater protection of public health and natural resources, the renewal and reuse existing civil infrastructure and Greenfields protection. While these benefits are numerous, the obstacles to Brownfields redevelopment are also very much alive. Redevelopment issues typically embrace a host of financial and legal liability concerns, technical and economic constraints, competing objectives, and uncertainties arising from inadequate site information. Because the resources for Brownfields redevelopment are usually limited, local programs will require creativity in addressing these existing obstacles in a manner that extends their limited resources for returning Brownfields to productive uses. Such programs may benefit from a structured and defensible decision framework to prioritize sites for redevelopment: one that incorporates the desired objectives, corresponding variables and uncertainties associated with Brownfields redevelopment. This thesis demonstrates the use of a decision analytic tool, Bayesian Influence Diagrams, and related decision analytic tools in developing quantitative decision models to evaluate and rank Brownfields sites on the basis of their redevelopment potential

Precipitable Water Comparisons Over Ghana using PPP Techniques and Reanalysis Data

Atmospheric Water vapor is an important greenhouse gas and contributes greatly in maintaining the Earth’s energy balance. This critical meteorological parameter is not being sensed by any of the 22 synoptic weather stations in Ghana. This study presents a highly precise tool for water vapor sensing based on the concept Global Navigation Satellite Systems (GNSS) meteorology and tests the computed results against global reanalysis data. Conventional approaches used to sense the atmospheric water vapor or Precipitable Water (PW) such as radiosondes, hygrometers, microwave radiometers or sun photometers are expensive and have coverage and temporal limitations. Whereas GNSS meteorological concept offers an easier, inexpensive and all-weather technique to retrieve PW or Integrated Water Vapor (IWV) from zenith tropospheric delays (ZTD) over a reference station. This study employed precise point positioning (PPP) techniques to quantify the extend of delays on the signal due to the troposphere and stratosphere where atmospheric water vapor resides. Stringent processing criteria were set using an elevation cut-off of 5 degrees, precise orbital and clock products were used as well as nominal tropospheric corrections and mapping functions implemented. The delays which are originally slanted are mapped unto the zenith direction and integrated with surface meteorological parameters to retrieve PW or IWV. The gLAB software, Canadian Spatial Reference System (CSRS) and Automatic Precise Positioning Service (APPS) online PPP services were the approaches used to compute ZTD. PW values obtained were compared with Japanese Metro Agency Reanalysis (JRA), European Centre for Medium-Range Weather Forecasts Reanalysis (ERA-interim) and National Center for Environmental Prediction (NCEP) global reanalysis data. Correlation analysis were run on the logged station data using the three approaches and global reanalysis data. The obtained results show stronger correlation between the retrieved PW values and those provided by the ERA-interim. Finally, the study results indicate that with a more densified network of GNSS base stations the retrieved PW or IWV will greatly improve numerical weather predictions in Ghana.Keywords: GNSS Signals, PPP, Integrated Water vapour, Precipitable Water, Reanalysis Model

Climate variations, urban solid waste management and possible implications for Anopheles mosquito breeding in selected cities of coastal Ghana

Climate-induced environmental changes are known to support prevalence of disease vectors and pathogens. Temperature, rainfall, humidity and other environmental variables are considered potential drivers of population dynamics of many vectors and pathogens of health importance, especially in the tropics. This study was conducted to understand the variability and trends in atmospheric temperature and rainfall, as well as how these factors may affect the breeding of Anopheles mosquitoes in the urban areas in the future. Accra and Sekondi-Takoradi Metropolitan Areas (AMA and STMA) of coastal Ghana were the selected study sites. Anopheles larvae were sampled from pre-identified breeding sites in the two cities. Atmospheric temperature and rainfall as measured by synoptic weather stations were collected for the two cities. Again, thirty years climate data on daily minimum and maximum temperature and rainfall for both cities from Ghana Meteorological Agency (Gmet) were employed in the study. Using a statistical downscaling approach, the average of the ENSEMBLE GCM outputs AR4-BCM2 and AR4-CNCM3 scenario A1B were downscaled to match with rainfall and temperature observations of AMA and STMA. Results showed that improper solid waste management in the cities promote the breeding of Anopheles mosquitoes. Climate data analysis showed that past rainfall in the cities were below average; in the future, however, up to year 2050, the cities may experience high rainfalls and temperatures above the average. Notably, significant increases may be observed in the total monthly rainfalls as well as a slight shift of rainfall pattern in the minor season. This implies that Anopheles mosquito breeding may no longer be seasonal in the cities but perennial and malaria transmission may also follow the same trend. Poor urban dwellers who find it difficult to adopt preventative measures will be prone to persistent malaria transmission. This will increase malaria transmission among vulnerable populations in urban areas. This study recommends that city authorities must intentionally work at lowering the surface temperatures in the cities through the growing of trees and also to regularly desilt drains in order to reduce the breeding of Anopheles mosquitoes

Mosquito breeding site water temperature observations and simulations towards improved vector-borne disease models for Africa

An energy budget model is developed to predict the water temperature of typical mosquito larval developmental habitats. It assumes a homogeneous mixed water column driven by empirically derived fluxes. The model shows good agreement at both hourly and daily time scales with 10-min temporal resolution observed water temperatures, monitored between June and November 2013 within a peri-urban area of Kumasi, Ghana. There was a close match between larvae development times calculated using either the model-derived or observed water temperatures. The water temperature scheme represents a significant improvement over assuming the water temperature to be equal to air temperature. The energy budget model requires observed minimum and maximum temperatures, information that is generally available from weather stations. Our results show that hourly variations in water temperature are important for the simulation of aquatic-stage development times. By contrast, we found that larval development is insensitive to sub-hourly variations. Modelling suggests that in addition to water temperature, an accurate estimation of degree-day development time is very important to correctly predict the larvae development times. The results highlight the potential of the model to predict water temperature of temporary bodies of surface water. Our study represents an important contribution towards the improvement of weather-driven dynamical disease models, including those designed for malaria early forecasting systems

A breeding site model for regional, dynamical malaria simulations evaluated using in situ temporary ponds observations

Daily observations of potential mosquito developmental habitats in a suburb of Kumasi in central Ghana reveal a strong variability in their water persistence times, which ranged between 11 and 81 days. The persistence of the ponds was strongly tied with rainfall, location and size of the puddles. A simple power-law relationship is found to fit the relationship between the average pond depth and area well. A prognostic water balance model is derived that describes the temporal evolution of the pond area and depth, incorporating the power-law geometrical relation. Pond area increases in response to rainfall, while evaporation and infiltration act as sink terms. Based on a range of evaluation metrics, the prognostic model is judged to provide a good representation of the pond coverage evolution at most sites. Finally, we demonstrate that the prognostic equation can be generalised and equally applied to a grid-cell to derive a fractional pond coverage, and thus can be implemented in spatially distributed models for relevant vector- borne diseases such as malaria

Precipitation variability and trends in Ghana: An intercomparison of observational and reanalysis products

Inter-annual variability and trends of annual/seasonal precipitation totals in Ghana are analyzed considering different gridded observational (gauge- and/or satellite-based) and reanalysis products. A quality-controlled dataset formed by fourteen gauges from the Ghana Meteorological Agency (GMet) is used as reference for the period 1961?2010. Firstly, a good agreement is found between GMet and all the observational products in terms of variability, with better results for the gauge-based products?correlations in the range of 0.7?1.0 and nearly null biases?than for the satellite-gauge merged and satellite-derived products. In contrast, reanalyses exhibit a very poor performance, with correlations below 0.4 and large biases in most of the cases. Secondly, a Mann-Kendall trend analysis is carried out. In most cases, GMet data reveal the existence of predominant decreasing (increasing) trends for the first (second) half of the period of study, 1961?1985 (1986?2010). Again, observational products are shown to reproduce well the observed trends?with worst results for purely satellite-derived data?whereas reanalyses lead in general to unrealistic stronger than observed trends, with contradictory results (opposite signs for different reanalyses) in some cases. Similar inconsistencies are also found when analyzing trends of extreme precipitation indicators. Therefore, this study provides a warning concerning the use of reanalysis data as pseudo-observations in Ghana.This study was supported by the EU project QWeCI (Quantifying Weather and Climate Impacts on health in developing countries), funded by the European Commission Seventh Framework Research Programme under the grant agreement 243964

A process-based validation of GPM IMERG and its sources using a mesoscale rain gauge network in the West African forest zone

Using a two-year dataset (2016–17) from 17 one-minute rain gauges located in the moist forest region of Ghana, the performance of Integrated Multisatellite Retrievals for GPM, version 6b (IMERG), is evaluated based on a subdaily time scale, down to the level of the underlying passive microwave (PMW) and infrared (IR) sources. Additionally, the spaceborne cloud product Cloud Property Dataset Using SEVIRI, edition 2 (CLAAS-2), available every 15 min, is used to link IMERG rainfall to cloud-top properties. Several important issues are identified: 1) IMERG’s proneness to low-intensity false alarms, accounting for more than a fifth of total rainfall; 2) IMERG’s overestimation of the rainfall amount from frequently occurring weak convective events, while that of relatively rare but strong mesoscale convective systems is underestimated, resulting in an error compensation; and 3) a decrease of skill during the little dry season in July and August, known to feature enhanced low-level cloudiness and warm rain. These findings are related to 1) a general oversensitivity for clouds with low ice and liquid water path and a particular oversensitivity for low cloud optical thickness, a problem which is slightly reduced for direct PMW overpasses; 2) a pronounced negative bias for high rain intensities, strongest when IR data are included; and 3) a large fraction of missed events linked with rainfall out of warm clouds, which are inherently misinterpreted by IMERG and its sources. This paper emphasizes the potential of validating spaceborne rainfall products with high-resolution rain gauges on a subdaily time scale, particularly for the understudied West African region