Urban lakes in a developing nation: Drivers, states and impacts of water quality and quantity in Dhaka, Bangladesh

Seasonal Dynamics in Bacteriological and Physicochemical Water Quality of the Southern Gulf of Lake Tana

Freshwater lakes are important sources of water for domestic, agricultural, and industrial uses. Lake Tana offers a range of ecosystem services to the surrounding communities. However, this lake is facing deterioration in water quality due to pollution caused by anthropogenic influences. Therefore, regular monitoring of key water quality parameters is critical to understanding the water quality status of the lake. This study aimed to assess the seasonal fluctuation of water quality of the Southern Gulf of Lake Tana using indicator bacteria and some physicochemical parameters. A total of 48 water samples were collected in dry and wet seasons from eight sites in the study area. Total coliforms (TC), faecal coliforms (FC), and some physicochemical parameters (pH, temperature, turbidity, electrical conductivity, total dissolved solids, dissolved oxygen, biochemical oxygen demand, ammonium, nitrate, and phosphate) were determined following standard methods. The results revealed that coliform counts were above the permissible level set by Ethiopian Standards and varied significantly among sites (P < 0.05). FC counts ranged from 1 to 1600 MPN/100 ml (with the lowest and highest mean value of 1 at site 8 to 1076.5 ± 3.1 at site 4) and again TC counts ranged from 1 to 1600 MPN/100 ml (with a mean value of 4.8 ± 1.81 at site 6 to 1600 at site 4 and site 8). The findings also confirmed that the highest counts of coliforms were observed during the wet season. The high counts are attributed to the discharge of human excreta and animal wastes during the rainy season from the different anthropogenic activities near the Gulf. Significant variations in most of the physicochemical parameters were also observed between sites and seasons. FC and biochemical oxygen demand (BOD₅₎ in most sites did not meet the EPA standard for surface water. Corrective measures are highly recommended for anthropogenic activities driving high pollution loads in the lake.

Predicting road quality using high resolution satellite imagery: A transfer learning approach

Recognizing the importance of road infrastructure to promote human health and economic development, actors around the globe are regularly investing in both new roads and road improvements. However, in many contexts there is a sparsity—or complete lack—of accurate information regarding existing road infrastructure, challenging the effective identification of where investments should be made. Previous literature has focused on overcoming this gap through the use of satellite imagery to detect and map roads. In this piece, we extend this literature by leveraging satellite imagery to estimate road quality and concomitant information about travel speed. We adopt a transfer learning approach in which a convolutional neural network architecture is first trained on data collected in the United States (where data is readily available), and then “fine-tuned” on an independent, smaller dataset collected from Nigeria. We test and compare eight different convolutional neural network architectures using a dataset of 53,686 images of 2,400 kilometers of roads in the United States, in which each road segment is measured as “low”, “middle”, or “high” quality using an open, cellphone-based measuring platform. Using satellite imagery to estimate these classes, we achieve an accuracy of 80.0%, with 99.4% of predictions falling within the actual or an adjacent class. The highest performing base model was applied to a preliminary case study in Nigeria, using a dataset of 1,000 images of paved and unpaved roads. By tailoring our US-model on the basis of this Nigeria-specific data, we were able to achieve an accuracy of 94.0% in predicting the quality of Nigerian roads. A continuous case estimate also showed the ability, on average, to predict road quality to within 0.32 on a 0 to 3 scale (with higher values indicating higher levels of quality).