As the global community begins to recognize the urgency and necessity of environmental stewardship, green infrastructure is becoming a key component in modern city planning. Varanasi, as both a part of its efforts to become a ‘smart city’ and for the sake of its residents, should give attention to improving its environment. Environmentally friendly updates to existing infrastructure and investing in projects that prevent further damage to the environment and help mitigate previous damage are a crucial component of this effort towards a more environmentally sustainable city.
The studio narrowed down environmental research efforts to three critical aspects:
- Rising temperatures potentially due to the Urban Heat Island effect,
- Poor air quality caused by pollution and lack of green space, and
- Dangerously poor water quality due to an outdated, run down, and overused sewer system.
The studio performed two analyses. The first, a Land Use Classification analysis used data from Sentinel and GIS to classify land as urban, vegetation, field, or water. The second, a Land Surface Temperature analysis, used imagery from Land Sat to classify surface temperatures from low to high.
Map of Unsupervised Land Classifications and Land Surface Temperature
By comparing the two analyses, zones in which Urban Heat Islands might exist were identified. Identifying and classifying these zones was an important step in identifying areas that have a more prominent need to install heat-mitigating infrastructure.
The results of the analysis comparison showed that the Assi Ghat area had little greenery throughout and pockets of high temperature along the riverfront. The neighborhood surrounding the rail station was mainly urban with a few moderately sized green spaces as well as a few large swaths of green space due to a military cantonment. Temperatures soared along the railroad, likely due to lack of vegetation along the railroad tracks. Sarnath, an area consisting almost entirely of fields and vegetation with a few small urban areas dotted across, was subject to moderate temperatures throughout most of the zone, likely due to high amounts of vegetation.
IIT-Kharagpur provided local data points that measured the air quality index at various points in Varanasi. The most relevant points to the three focus zones indicated that air quality in the Assi Ghat zone was quite poor, with an index hovering between 200 and 300. The air quality around the rail station was poor as well, but air quality was better at Sarnath, likely due to its location outside of the city and a high amount of vegetation.
Air Quality Map
As of 2005, at least 200 million liters of ‘raw, untreated sewage’ is directed into the Ganges each day (Hamner et al), and according to the Varanasi City Sanitation Plan, up to 70% of the city is not covered by the sewer network. The existing drainage system is old and unlined, often causing cross-contamination with clean ground water (City Sanitation Plan). Because Varanasi lacks a separate drainage system for storm water, sewers tend to flood under the combined pressure of storm water and local sewage, releasing untreated sewage onto residential streets and sidewalks (Hamner et al). In the summer, the 3-month monsoon season floods the sump wells of the sewage pumping stations, forcing them to close for the duration of the monsoons and causing raw sewage “to flow directly into the river” (Hamner et al). The pollution in the Ganges river is so severe that the incidence of water-borne disease in Varanasi is 66%. Varanasi sees an average of 33 (documented) cases of cholera per year. Water from the Ganges is used to bathe, wash clothes, brush teeth, cook, etc. (Hamner et al) – it is a daily life-source for the residents of Varanasi who do not have an alternative source of water. Thus, the health of this river is vital to the health of the people.
Map of Existing and Proposed Sewer Network
This studio aims to improve the quality of life for Varanasi’s residents by providing recommendations that will improve the environment surrounding their places of work, residences, and cultural establishments. While there are many environmental challenges to be addressed in Varanasi, these three areas – the Urban Heat Island, poor air quality, and poor water quality – can be addressed starting now through small-scale changes. This data collection was the first step towards recommendations for small-scale changes. The ultimate goal is to use the city’s existing infrastructure to implement affordable environmental solutions that local citizens can easily learn about, visualize, and support.
Mobility in Varanasi is a pressing concern due to the declining transportation infrastructure and the exponential growth in vehicular ownership. Lack of separated carriageways for vehicles and pedestrians raises safety concerns. Even though there has been a growing interest in transportation improvement projects, such as the flyover construction near the railway station and multiple proposals for widening major arterials, there is no coordinated traffic management plan that addresses the congestion issues.
The research conducted in this sub-discipline primarily revolves around a congestion analysis given a set of origins and destinations (O-Ds). The data collection procedure uses a Bing API to extract typical travel times and congested travel times during the AM peak hour for the set of origins and destinations. The objective of this analysis is to observe high congestion routes between high activity locations and popular points of interest in each of the three zones. The origins were by default assigned as the top 60 hotels and hostels in Varanasi, based on the assumption that most tourist trips originate from a hotel. Tourist attractions, like the ghats and famous temples within each zone, were chosen as the destinations.
The difference between the congested travel time and typical travel time gives the excess delay that commuters experience in Varanasi. Based on the travel times observed, the Bing API also provides the most optimal route that vehicles use to navigate between the said origin and destination. Simply put, this process is like using Google or Bing Maps as an individual to get to any destination from your location, aggregated at the trip level for multiple O-D pairs. The collection of all the routes observed provides insight into the most frequently used roads for movement and accordingly, the most congested corridors in the city