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CO emissions from urban traffic are a major concern in an era of increasing ecological disequilibrium. Adding to the problem net CO emissions in urban settings are worsened due to the decline of bio-productive areas in many cities. This decline exacerbates the lack of capacity to sequestrate CO at the micro and meso-scales resulting in increased temperatures and decreased air quality within city boundaries. Various transportation and environmental strategies have been implemented to address traffic related CO emissions, however current literature identifies difficulties in pinpointing these critical areas of maximal net emissions in urban transport networks. This study attempts to close this gap in the literature by creating a new lay-person friendly index that combines CO emissions from vehicles and the bio-capacity of specific traffic zones to identify these areas at the meso-scale within four ranges of values with the lowest index values representing the highest net CO levels. The study used traffic volume, fuel types, and vehicular travel distance to estimate CO emissions at major links in Dhaka, Bangladesh's capital city's transportation network. Additionally, using remote-sensing tools, adjacent bio-productive areas were identified and their bio-capacity for CO sequestration estimated. The bio-productive areas were correlated with each traffic zone under study resulting in an Emission Bio-Capacity index (EBI) value estimate for each traffic node. Among the ten studied nodes in Dhaka City, nine had very low EBI values, correlating to very high CO emissions and low bio-capacity. As a result, the study considered these areas unsustainable as traffic nodes going forward. Key reasons for unsustainability included increasing use of motorized traffic, absence of optimized signal systems, inadequate public transit options, disincentives for fuel free transport (FFT), and a decline in bio-productive areas.

Genetic algorithms, p-Median problem, Alternating location-allocation heuristic, Hybrid algorithms, Continuous space,

Green spaces can facilitate sustainable urban environment in a number of ways: purifying air and water, filtering noise, and stabilizing the microclimate. Therefore, city planners have to design optimal sites to provide new green spaces. The present study addresses the genetic-algorithm-based multiobjective modeling of optimal sites for multitype green spaces considering multiple objectives. A new model has been developed and applied to identify the optimum sites for green spaces, particularly parks and open spaces (POSs). We considered six criteria: population, air quality, noise level, air temperature, water quality, and recreational value, including barriers for placing new POSs. The model thus developed was applied to Dhaka as a case study. The spatial functions of GIS are used to quantify, analyze, and represent the six objective criteria of our model. The modeling results show a successful optimization of locations for new POS. In addition, a suitability analysis is performed to find locations of various POSs using GIS. This study provides an indication of how to site multitype green spaces to make a sustainable urban environment.