This study was conducted to measure the impact of a municipal solid waste landfill on groundwater quality around Njelianparamba, a solid waste dumping site in Kozhikode district, Kerala state, India. One of the major problems associated with dumping of municipal solid waste landfill is the release of leachate and its impact on surrounding groundwater. In this study, physico-chemical and bacteriological parameters of groundwater samples collected from the region surrounding the leachate area during the pre- and post-monsoon seasons were analysed. The majority of the groundwater samples contained contaminants at a level beyond the permissible limit set by the Bureau of Indian Standards for drinking water quality. The Geographic Information System software of the Environmental Systems Research Institute, (USA) ArcMap 10.1 was used to prepare spatial distribution maps of different parameters and Leachate Pollution Index and Water Quality Index in the study area were applied to assess the overall quality of groundwater. Characterisation of leachate and groundwater samples revealed that, water in the domestic wells has been deteriorated in response to the percolation of leachate. Additionally spatial and correlation analysis revealed that contamination was present maximum within 300 m radius around the landfill site.
Groundwater has long been considered as an important water source owing to its relatively low susceptibility to pollution and large storage capacity. Groundwater is comparatively safe and reliable when compared with surface water [
Rapid industrialisation, growing population and changing life style are the root causes of increasing solid waste generation in developing countries. In India, about 0.15 million tones of solid waste are generated daily [
Protection of groundwater is a major environmental issue since the importance of water quality on human health has attracted a great deal of interest in recent years [
The pollution potential of a particular landfill can be assessed through various indices. Environmental indices such as the Water Quality Index (WQI) and Leachate Pollution Index (LPI) have been developed to determine the extent of pollution. The potential of leachate from different landfills to contaminate local systems can be evaluated using an index known as LPI [
Njelianparamba, a solid waste dumping site of Kozhikode City, India is situated 9 km from the city. An average of 200 tonnes of waste per day is dumped in to 18 hectare area. The dumping site is located at 11˚13′30″N to 11˚11'N and 75˚48′E to 75˚50′30″E. The area is one of the primary industrial areas of the Kozhikode district. A number of small, medium and large industrial units on clay, agro-forestry, chemical and metals are located in and around the site. The height of the dump is about 3 to 4 m above ground level and average of 60 - 80 tonnes of organic waste (vegetable, meat and fish waste) from markets and households are deposited in to the dump daily. The landfill originally accepted only non-hazardous solid wastes but now receives both degradable and non-degradable waste including hazardous waste. Organic solid wastes are treated at the waste treatment plant at Njelianparamba. However, there is no leachate treatment facility in the dump yard. The leachate from the plant and trench yard is collected in a pond on the north east side of the plant.
The study area is characterized by a humid tropical climate with high rainfall. The climate is divided in to four seasons―summer, south west tropical monsoon period (SW), north east tropical monsoon period (NE) and winter. The SW and NE monsoons are responsible for 82.77% of the total rainfall in the area. June to November is the rainy season in the study area (monsoon season) during which time about 70% of the rainfall is contributed by the SW monsoon. The average annual rainfall recorded in the area during the study period is 2777 mm [
The geological formations of Njelianparamba primarily consist of porous laterite and forms potential phreatic aquifers; it comes under the midland terrain of Kozhikode district [
The sampling and analysis of a leachate sample and 18 groundwater samples were conducted during November 2013 (post-monsoon) and May 2014 (pre-monsoon). A random sampling method was used to collect groundwater samples within a 0.5 km radius of the landfill site and examine its impact on the groundwater quality. The samples were collected only from eastern side of the solid waste treatment plant; no well was identified in the western side of the plant. Pre-cleaned polyethylene bottle (1 L) were used to collect the leachate samples from the drains of the dumping site and groundwater samples from wells around the landfill site. The pH, electrical conductivity and dissolved solids were recorded on site at the time of sampling with a multi-parameter PCSTestr35. To analyze biological oxygen demand (BOD), samples were collected in 300 ml BOD bottles and dissolved oxygen was fixed onsite (Modified Winkler’s method). The total hardness, Ca2+, Mg2+, Cl− and total alkalinity were analyzed by titrimetric methods [
The base maps for generating the study maps were collected from the Soil Survey Department of the Kozhikode district. The map of Njelianparamba was digitized and various findings were spatially represented using the ArcMap 10.1 software. A GARMIN GPS was used to record the latitude and longitude of sampling points which were imported into the GIS platform. The interpolation technique, Inverse Distance Weighting (IDW) was used for the spatial modelling of the study results. IDW is an algorithm used to interpolate data spatially or estimate values between measurements. The distribution of total coliform, fecal coliform, E. coli and variations in the dissolved solids with distance from landfill site in groundwater samples of the study area are represented through interpolated GIS maps that were processed by the IDW method.
The results of physico-chemical analyses of the leachate samples are compared with the National standards set by Ministry of Environment and Forests, Government of India [
Parameters | Pre-monsoon | Post-monsoon | Leachate disposal Standard (MoEF 2000) |
---|---|---|---|
pH | 5.02 | 4.54 | 5.5 - 9.0 |
TDS | 16300 | 14300 | 2100 |
Chloride | 8483 | 4954 | 1000 |
COD | 36000 | 34012 | 250 |
BOD | 11022 | 10230 | 30 |
792 | 532 | - | |
111 | 101 | - | |
F− | 0.6 | 0.52 | 2 |
Na+ | 2872 | 2042 | - |
K+ | 3536 | 3399 | - |
Fe | 30 | 29 | - |
Cu | 0.35 | 0.29 | 3 |
Zn | 1.6 | 1.4 | 5 |
Cd | 0.1 | 0.12 | 2 |
Ni | 1.12 | 1.0 | 3 |
Pb | 0.23 | 0.22 | 0.1 |
All values are in mg/L, except pH, EC (in μS/cm).
4.54 in the pre- and post-monsoon seasons respectively, indicating the leachate is acidic in nature. The pre- and post-monsoon dissolved solids were 16300 mg/L and 14300 mg/L respectively which were considerably high than the concentration set by the Ministry of Environment and Forests, discharge standard for leachate disposal. The higher value of dissolved solids in the samples is probably due to the large concentration of cations and anions which indicated the presence of inorganic materials. The high BOD and COD indicate the high organic pollution. Leachate contained high levels of chloride that exceed the recommended standards for leachate disposal. Because chloride is inert and non-biodegradable, it can be used as an indicator of contamination [
High nitrate concentration is primarily due to domestic waste. The high concentration for sodium and potassium around the landfill indicate impact of leachate. While the high concentration of iron reflects dumping of metal scrap and tin. The color of leachate is dark brown which possibly originated during the oxidation of ferrous to ferric form leading to the formation of ferric hydroxide colloids and compounds with fulvic and humic substances [
The LPI (Leachate Pollution Index) provides a proficient method for evaluating extent of leachate pollution from landfill sites. This index is a comparative and quantitative measure of leachate pollution potential that can be efficiently applied to areas prone to leachate migration and subsequent groundwater pollution. To determine the LPI, the sub-index values must be calculated based on the concentration of the leachate pollutants obtained from the sub-index curves for the pollutant variables. The weights for these parameters were calculated based on the significance levels of the individual pollutants. The p values obtained were multiplied by the respective weights assigned to each parameter to determine the LPI using the Equation (1) [
where LPI = the weighted additive leachate pollution index, Wi = the weight for the ith pollutant variable, Pi = the sub index value of the ith leachate pollutant variable, n = number of leachate pollutant variables used in calculating LPI
However, if data for all leachate pollutant variables included in LPI is not available, the LPI can be calculated using the dataset of the available leachate pollutants. In such case, the LPI can be calculated by the Equation (2)
where m represents the number of leachate pollutant variables for available data, but in that case, m < 18 and
The contamination potential of leachate can be calculated in terms of LPI. The calculated LPI of Njelianparamba dumping sites were 28.81 and 25.09 in the pre-and post-monsoon seasons respectively, as given in
Leachate Constituents | Mean value | Individual pollution rating Pi | Weight Wi | Overall pollution rating PiWi | ||||
---|---|---|---|---|---|---|---|---|
Pre- monsoon | Post- monsoon | Pre- monsoon | Post- monsoon | Pre- monsoon | Post- monsoon | Pre- monsoon | Post- monsoon | |
pH | 5.02 | 4.54 | 8 | 8 | 0.055 | 0.055 | 0.44 | 0.44 |
TDS | 16300 | 14300 | 38 | 35 | 0.050 | 0.050 | 1.9 | 1.75 |
Chloride | 8483 | 4954 | 79 | 40 | 0.048 | 0.048 | 3.79 | 1.92 |
COD | 36000 | 34012 | 82 | 81 | 0.062 | 0.062 | 5.08 | 5.02 |
BOD | 11022 | 10230 | 66 | 64 | 0.061 | 0.061 | 4.03 | 3.90 |
Ammonia Nitrogen | 111 | 101 | 10 | 10 | 0.051 | 0.051 | 0.51 | 0.51 |
Fe | 30 | 29 | 5 | 5 | 0.045 | 0.045 | 0.23 | 0.23 |
Cu | 0.35 | 0.29 | 5 | 5 | 0.050 | 0.050 | 0.25 | 0.25 |
Zn | 1.6 | 1.4 | 5 | 5 | 0.056 | 0.056 | 0.28 | 0.28 |
Ni | 1.12 | 1.0 | 5 | 5 | 0.052 | 0.052 | 0.26 | 0.26 |
Pb | 0.23 | 0.22 | 5 | 5 | 0.063 | 0.063 | 0.32 | 0.32 |
Total | 0.593 | 0.593 | 17.08 | 14.88 | ||||
LPI | 28.81 | 25.09 |
All values are in mg/L except pH.
season [
The physico-chemical composition of groundwater samples in the pre-monsoon and post-monsoon seasons was statistically analyzed and the results provided in
Groundwater contamination can be traced by considering excess chloride ions as an index of pollution [
Water quality Parameters | Pre-monsoon | Post-monsoon | Desirable Limit (BIS 2012) | ||||||
---|---|---|---|---|---|---|---|---|---|
Max | Min | Mean | SD | Max | Min | Mean | SD | ||
pH | 7.68 | 4.76 | 6.60 | 0.99 | 7.13 | 4.36 | 6.01 | 0.88 | 6.5 - 8.5 |
EC | 1644 | 292 | 939.39 | 418.51 | 1487 | 181 | 621.11 | 305.8 | - |
TDS | 1170 | 202 | 665.39 | 297.94 | 994 | 130 | 484.44 | 274.79 | 500 |
440 | 44 | 78.26 | 112.28 | 272 | 19 | 64.83 | 67.66 | 200 | |
Cl− | 620 | 44 | 210.44 | 148.64 | 310 | 3.92 | 197.67 | 96.09 | 250 |
TA | 357 | 3.40 | 160.18 | 127.02 | 503.23 | 42.58 | 118.50 | 115.17 | 200 |
TH | 524 | 34.90 | 251.87 | 154.35 | 440 | 44 | 176.22 | 112.28 | 200 |
Ca2+ | 116 | 9.31 | 55.71 | 34.48 | 164.16 | 6.8 | 47.91 | 44.01 | 75 |
Mg2+ | 84.85 | 0.94 | 27.34 | 22.64 | 38.88 | BDL | 15.43 | 12.54 | 30 |
Na+ | 294 | 26.0 | 128.56 | 71.74 | 112 | 11.20 | 69.30 | 32.20 | - |
K+ | 364 | 3.63 | 68.92 | 91.62 | 38.88 | 3.3 | 42.36 | 12.54 | - |
COD | 264 | 48 | 124.0 | 59.06 | 220 | 24 | 100.0 | 56.46 | - |
95.63 | 1.35 | 27.42 | 23.06 | 83 | BDL | 21.50 | 0.88 | 45 | |
Fe | 0.67 | BDL | 0.15 | 0.42 | 0.52 | BDL | 0.08 | 0.23 | 0.30 |
Cu | 0.04 | BDL | 0.011 | 0.02 | 0.59 | BDL | 0.12 | 0.19 | 0.05 |
Zn | 0.22 | BDL | 0.06 | 0.04 | 0.03 | BDL | 0.0 | 0.01 | 5.0 |
Mn | 0.23 | 0.03 | 0.07 | 0.06 | 0.18 | BDL | 0.05 | 0.07 | 0.10 |
Cd | 0.02 | BDL | 0.01 | 0.003 | 0.004 | BDL | 0.001 | 0.003 | 0.003 |
All values are in (mg/L) except EC (μS/cm) and pH, BDL―Below Detection Limit.
gastric carcinomas [
Almost all samples contained concentration of major cations exceeding their limits. The hardness of samples was found to range from 35 to 524 mg/L and 44 to 440 mg/L in the pre- and post-monsoons, respectively. Most of the sample stations reported hardness values exceeding the maximum desirable limit of 200 mg/L prescribed by BIS. High levels of hardness may affect water supply system resulting in excessive soap consumption, calcification of arteries and cause urinary concretions, diseases of kidney bladder and stomach disorder [
The groundwater samples were analysed for Cu, Fe, Mn, Cd and Zn. Iron levels in the groundwater ranged from BDL to 0.67 mg/L and 0.52 to BDL in the pre- and post-monsoon seasons, respectively. The concentration of iron exceeded in 61% of the samples collected from the study area. Cu and Zn were found to be within the permissible limit prescribed by the BIS. The concentration of Mn exceeded the limit in the sample collected from NP-8, NP-9 and NP-18. The Cd concentrations of the sample were ranged from BDL to 0.02 mg/L and BDL to 0.004 mg/L in the pre- and post-monsoon seasons, respectively.
The bacteriological analysis of the groundwater quality was spatially represented and analysed in the form of GIS maps. The distribution of bacteria and the distance between the well and landfill site were the two major criteria used to prepare the maps. The distribution of total coliform, fecal coliform and E. coli bacteria was represented through interpolated GIS maps that were processed by the Inverse Distance Weighting (IDW) method. As shown in
The Canadian Council of Ministers of the Environment introduced an index to determine water quality (CCME WQI). This index provides a suitable method to aggregate a complex water quality data that can be understood easily by the public, policy makers, planners and water distributors [
The factor of 1.732 has been introduced to scale the index from 0 to 100, where zero signifies very poor water quality and values close to 100 signify excellent water quality. The water quality is ranked in the following five categories shown below. A WQI map was created using the CCME WQI classification to understand the groundwater quality.
Excellent: (CCME WQI values 95 - 100)
Good: (CCME WQI values 80 - 94)
Fair: (CCME WQI values 60 - 79)
Marginal: (CCME WQI values 45 - 59)
Poor: (CCME WQI values 0 - 44)
The overall water quality in the study area was represented using CCME WQI. CCME WQI of the study area was calculated in the pre- and post-monsoon seasons. According to CCME WQI, six sampling sites (NP-1, NP-5, NP-6, NP-8, NP-9 and NP-18) showed poor WQI values. All the six sites were located 200 m from the dumpsite. Additionally, eight sites showed marginal water quality and four stations showed fair water quality. A CCME WQI map was created using the CCME WQI classification to understand the groundwater quality in the study area.
The spatial variation of the dissolved solids in groundwater samples and distances from the landfill to the study area were represented using Geographic Information System. Samples were collected spatially at different distances from the landfill site. These two criteria were used to determine the groundwater quality at the sampling
sites with proximity to the landfill.
Groundwater samples from this zone had low TDS. The zonation map showed that the sampling sites within zone I and II contain more soluble salts in groundwater and cannot be used for any purpose. As shown in
Correlation analysis is a descriptive technique to assess the degree of association among variables. Statistical package for Social Sciences (SPSS version 19.0) was used for correlation analysis. In this study, Pearson correlation coefficients were determined for various water quality parameters.
Parameters | Distance | Depth | TDS |
---|---|---|---|
Distance | 1.0 | 0.416 | −0.863 |
Depth | 0.416 | 1.0 | −0.510 |
TDS | −0.863 | −0.510 | 1.0 |
Chloride | −0.733 | −0.516 | 0.854 |
Correlation > ±0.6 are in italics.
contaminants in groundwater normally decreases with increasing distance from pollution. A moderately high negative correlation was obtained for TDS and chloride with well depth, which also indicated that the concentration of contaminants in groundwater samples decreased with increasing depth. Correlation analysis confirmed that groundwater quality improved with increases in well depth and the distance of the well from the pollution source.
The impact of landfills leachate on the surrounding groundwater quality in Njelianparamba, India is a major environmental concern of the area. In this study, physico-chemical and bacteriological parameters of leachate and groundwater samples collected in and around the landfill site were analysed. The results showed that the wells in close proximity to the landfill (NP-5, NP-6, NP-8, NP-9 and NP-18) were most affected by leachate percolation. Spatial distribution of groundwater quality parameters was measured by GIS. LPI and WQI in the study were applied to assess the overall quality of the leachate and groundwater. This method appears to be more systematic and provides a comparative evaluation of the quality of sampling sites. The LPI value at Njelianparamba for both the seasons exceeded the standard LPI of 7.4 proposed for leachate disposal. CCME WQI map was also generated using the same technique to understand the water potability spatially. The CCME WQI indicted that majority of the study area had poor and marginal water quality. However, the quality improved with increase in distance of the well from the pollution source. The majority of the parameters showed an inverse relationship between concentration and distance. The results of this study indicated that the Njelianparamba municipal dumping site was prone to groundwater contamination through leaching. Because dumping is a continuous process, without proper treatment facilities, groundwater in the surrounding area will gradually become more adversely by this activity.
JaseelaChonattu,KavyaPrabhakar,Harikumar Puthenveedu SadasivanPillai, (2016) Geospatial and Statistical Assessment of Groundwater Contamination Due to Landfill Leachate—A Case Study. Journal of Water Resource and Protection,08,121-134. doi: 10.4236/jwarp.2016.82010