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A study was carried out to address distribution of some heavy metals in deep groundwater resources of the Kathmandu Valley. Groundwater samples were analyzed for pH, ORP, EC, iron, manganese, zinc, and arsenic in 41 deep groundwater wells during pre monsoon and post monsoon seasons for two consecutive years. The study showed elevated concentrations of iron and manganese in the groundwater of the valley. The occurrence of elevated concentrations of arsenic was also exhibited and observed up to 0.160 mg/L. The spatial distribution patterns demonstrated elevated levels of EC, iron, manganese, zinc, and arsenic in central groundwater district (CGWD) of the valley. The monitored parameters except ORP are not significantly correlated with studied time series, inferring similar distribution of the metals. Correlation analysis and principal component analysis (PCA) were performed to find out relationships among examined parameters and metals. The ORP has strong negative correlations with iron, manganese, and arsenic, suggesting reductive mobilization mechanism of the metals in the groundwater. PCA results showed that iron and manganese with high positive loading factors were due to common natural source of origin of these metals in the groundwater, while negative loading factors of pH and ORP indicated that iron and manganese mobilization was favorable in low pH and reducing environment. Cluster analysis (CA) evidenced high mineralization in most of the wells in the CGWD.

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This study was focused to prepare fire hazard map of Bardia National Park (BNP) through fire hazard zonation model. BNP is located at western Terai (lowland plains) of Nepal. The model was prepared by using four sub-models. The sub-models were prepared by considering nine parameters selected on the basis of their significance in fire risk assessments and their data availability. Remotely sensed data and Geographic Information System (GIS) were used during model preparation and validation process. The output map from the model was validated using Moderate Resolution Imaging Spectroradiometer (MODIS) active fire points (2001- May 2014) within BNP. Very high fire risk areas were identified along southern boundary; Northeast and Northwest part of the park covering about 5% of the park area. Similarly, about 31% of the park area was identified as high risk zone and 31.43% area of BNP was identified as medium risk zone. MODIS active fire data was very useful for validation process that showed proportionate result with the area coverage of predicted risk classes. The validation result suggests that this prepared model can be used to prepare fire hazard zonation maps and preparedness plans of areas similar to BNP. The plans to prevent and control wildfire in BNP should be focused on high risk areas through preparedness initiatives, awareness activities and capacity building of fire fighting team. Reducing surface fuel load and fuel continuity during pre-fire season (Dec-Feb) in general and particularly during February shall reduce fire occurrences and its damage to a great extent.

A study was conducted in forty-one deep groundwater and twenty shallow groundwater wells of Kathmandu Valley, Nepal to assess arsenic contamination (shallow and deep groundwater) and spatial and seasonal variation in deep groundwater. The depths of the wells were ranged from 9 to 304 m. Groundwater samples were collected during pre monsoon and post monsoon in 2012. Atomic Absorption Spectrometer (AAS) was used to measure the concentration of arsenic. In pre monsoon and post monsoon, 36.59 % and 31.70 % of deep groundwater wells, respectively exceeded permissible values of World Health Organization guideline value of 0.01 mg/L for drinking water. The arsenic varied spatially with high concentration towards central groundwater district. Negative correlation between arsenic and ORP showed reductive arsenic mobilization mechanisms in deep groundwater. There was very weak negative correlation between arsenic concentration and depth of deep groundwater wells. The t-test revealed that there is significant difference in concentration of arsenic in between shallow and deep ground water with higher values of arsenic in deep groundwater.