Environment & Ecosystem Science (EES)

There is now a lot of concern throughout the world about the rise in atmospheric carbon dioxide and how it could alter the climate. An efficient way to reduce elevated atmospheric carbon dioxide concentrations and help slow down global warming has been to expand the area covered by plantation forests. The aim of this study is to estimation of carbon stock variation and how they affect the biomass of selected tree species in the study area. A systematic sampling method was employed for this study. There were taken a total of 60 sample plots from this study area. To collect soil and litter samples, five 1 m2 subplots were laid out inside the main plots. From this study, the carbon pools in Cupressus lusitanica species, such as aboveground carbon, belowground carbon, litterfall carbon, and soil organic carbon were 159.46±70.91, 41.46±18.44, 0.07±0.01 and 111.37±8.52 t C/ha, respectively. From Eucalyptus globulus, such as aboveground carbon (AGC), belowground carbon (BGC), litterfall carbon (LBC), and soil organic carbon (SOC), were 170.63±75.21,44.37±19.55, 0.02±0.003, and 141.07±22.88 t C/ha, respectively. The total mean carbon storage of Cupressus lusitanica was 312.36±97.88 t C/ha, and that of Eucalyptus globulus was 356.09±117.64 t C/ha. This was equivalent to 1146.36±359.23 t C/ha CO2 (gas) of Cupressus lusitanica and 1306.85±431.75 t C/ha CO2 (gas) of Eucalyptus globulus. The over-all mean carbon storage of Eucalyptus globulus was higher than that of Cupressus lusitanica in all carbon pools, with the exception of litterfall biomass. The over-all carbon storage of the species from this research area can be characterized as intermediate when related to other research carried out elsewhere in the tropics.

KEYWORDS: Eucalyptus globulus, Cupressus lusitanica, carbon stock, plantation forest, Above and below ground biomass

1. INTRODUCTION

The destruction of forests around the world announcements carbon dioxide into the atmosphere, which underwrites to climate change (Nunes et al., 2020). Extended changes in the global average weather patterns have been referred to as climate change (Seneviratne et al., 2021).There is now a lot of concern throughout the world about the rise in atmospheric carbon dioxide (CO2) and how it could alter the climate (Dar et al., 2015). An efficient way to reduce elevated atmospheric carbon dioxide (CO2) concentrations and help slow down global warming has been to expand the area covered by plantation forests (Aba et al., 2017). According to a recent assessment, between 2005 to 2010, carbon reserves in forestry biomass declined by an assessed 0.5 gigatons per year (Köhl et al., 2015). Forfeiture of forest biomass over disforestation and forest dilapidation make up 12%–20% of yearly glasshouse gas emissions (Kumar et al., 2022).The buildup of carbon in the atmosphere is a global problem since it is the main driver of global warming and has a profound impact on ecosystems, aquatic characteristics, and ocean level rise. About four billion hectares of forest cover make about 31% of the world’s land area, and its biomass alone stores 289 gigatons of carbon(FAO, 2011).

African forests contribute 21% of the global forest biomass carbon pool and store up to 630 kg of carbon per hectare annually, making them a vital vibration absorber for climate change (Katerere et al., 2009). Due to extensive deforestation, Ethiopia’s forest cover decreased from 13.78% to 11.40% between 1990 to 2015 (FAO, 2015). Compared to other sub-Saharan African nations, where the average yearly deforestation rate was 0.8%, this one had a high rate of approximately 1% (FAO, 2010). The aboveground carbon pools of tropical forests contain more carbon per unit area than any other type of land cover when they are in their natural state (Tyukavina et al., 2015). The active biomass of trees and understory plants, along with the dead mass of litter, woody debris, and soil organic matter, are the primary carbon reservoirs in tropical forest ecosystems. However, natural forests store more carbon above ground than any other vegetation (Borma et al., 2022). To combat global warming and effectively balance increasing atmospheric carbon dioxide (CO2) concentrations, plantations forests were established (Bernal et al., 2018).

The global carbon cycle depends on how forests respond to growing atmospheric carbon dioxide (CO2) concentrations (Xiao et al., 2021). Forests store more carbon than any other terrestrial ecosystem, storing over 70% of all soil organic matter and over 80% of all terrestrial above-ground carbon (Alemu, 2014). The burning of fossil fuels and the conversion of forests to agricultural land are the primary causes of the 3.5 pentagrams (1015 gramme or billion tones) of carbon that has accumulated in the atmosphere annually over the past century (Paustian et al., 2000). Deforestation, soil erosion, changes in land cover, and land use all have an impact on the carbon stock density in the forest ecosystem. Slope, altitude, and land use types all have an impact on the carbon reservoirs in the forest ecosystem (Assefa et al., 2017). Impact of slope on above- and belowground biomass, soil organic carbon, and total carbon in forest ecosystems (Yohannes et al., 2015). By accelerating carbon emissions, land use in the tropics has an impact on the worldwide carbon cycle. The density of soil organic carbon pool decreased by 20–50% when forests were converted to other land uses or land-living shelter into farmed land use types (Ahirwal et al., 2022).

The properties of stand stage and tree species composition on carbonstorage in forest ecosystems are appropriately taken into justification(Kaushal et al., 2021). According to with increasing stand age, morebiomass is accumulated (Zhang et al., 2012). It has not been well studiedhow certain tree species affect the storage of carbon in Ethiopianplantation forests. There has been very little research in Ethiopia’s forestcarbon pool that takes into account the tree species and environmentalconditions that influence carbon stock. Due to their rapid growth, speciesof plantation forests like E. globulus and C. lusitanica were essential toefforts to allay and familiarize to climate change over carbonrequisitioning and storage. Additionally, by lessening the strain onruminant natural forests, these quickly growing tree varieties wereextremely important. This study was among the few that addressed theseresearch gaps. This study offered experimental evidence towardsdeliberate these fast-rising tree species as a basis of carbon pools forfounded climate alteration extenuation approaches in this research area,specifically in Goncha Siso Enesie District, and nationwide. For targetedspecies, the data can be applied as initial information for sustainable forestmanagement. Additionally, the findings will aid in the understanding ofcarbon routine difference of the four mainly carbon pools such as,aboveground, belowground, litterfall, and soil organic carbon in speciesspecificplantations in Ethiopia, however, has not received much attentionfrom the fast-growing tree species the targeted study area. Theassessment Enhancing estimates of forests’ potential to store carbon andproviding the basis for national forest management depend on this data.Prior research conducted in Ethiopia and other tropical regions has showna great deal of interest in assessing the quantity of carbon deposited in thesoil and standing biomass of forest ecosystems (Negash and Starr, 2015).For foreign and district government, politicians, and other preservationadministrations, this further illustrates the gap in the plantation forest’scapacity to store carbon and provides some helpful information in currentcondition, including E. globulus and C. lusitanica tree species. This studyaddresses the information gaps regarding the estimation of carbon stockvariation and how they affect the biomass of selected tree species in thisresearch area. Specifically, to determine and contrast the carbon stock ofselected plant species above and below ground, to calculate the carbonstorage of litterfall under specific plant species, and to calculate the carbonstock of soil organic matter under specific plant species.

2. MATERIALS AND METHODS

2.1 Description of Study Area

The study was conducted in the Goncha Siso Enesie District in Amhara,Ethiopia. It is far from 335 km from Addis Ababa and 155 km from BahirDar. The study area is situated at 10° 27′ 36” and 11° 53′ 52” northlatitudes and 37° 12′ 56”, 38° 43′ 45”, 3° 55′, and 38° 24′ east longitudeswith an average altitude of 2000 meters above sea level (m.a.s.l.) (Fig. 1)

2.1.1 Climate and Agro-ecological Condition

Agro-ecological conditions and climate are two of the most importantvariables in deciding if a given place is suitable for a given settlement.Highland or Dega (12%), Midland or Woyna Dega (48%), and Lowland orKola (40%), are the district’s three agro-ecological zones. Rainfallaverages between 800 to 1800 mm annually. However, the unimodalrainfall pattern, which normally lasts from June to September, is erraticand insufficient, especially for timely-planted long-season crops. Thetemperatures of Woyna Dega and Dega were 18-25oC, as were thevegetation type, socioeconomic activities, and human agro-ecologicconditions. The temperature gets very hot from March up to May, when it rises above 25⁰C. Generally, according to the Woreda Office of Agricultureand Rural Development, the rainfall pattern of major crops stops duringthe time when most crops are in vegetative growth or at flowering stages(WoARD, 2019).

2.1.2 Study Site Selection

The selection of the study site was thoughtful; selected species wereplanted in nearby locations with comparable climatic, topographic, andedaphic conditions at the time of plantation, which is why the study siteselection was deliberate. The Goncha Siso Enesie District plantationsforest was chosen for the study because it offered the optimum location inthis regard. The team leader of Natural Resources Managementparticipated in an initial conversation to gain more knowledge about inthis plantations forest. Finally, to estimation the biomass carbon storagevariation and soil carbon stores of the target tree species were employeda well-defined boundary.

2.2 Sampling Design and Forest Inventory

For this investigation, systematic sample strategy stayed employed todemeanor the forest record in these plantation forests. Because it is easyto use in the field and yields more precise findings. It was found that theplot and the transect line were 50 and 100 meters apart, respectively.According to Genene et al. (2013) suggest that compared to circular plots,square plots can be more cost-effective, contain greater within-plotvariance, and are more representative of the same area. As a result, squaresample quadrants with dimensions of 10 m x 10 m (100 m2) wereconsidered for plantation measurements. Five 1m x 1m (1m2) subsamplingunits were set up inside each 10m x 10m quadrate at the fourcorners and at the center of each square plot to collect soil and litterfallsamples. To collect data on tree stands based on DBH and overall height ofthe trees, a wood inventory stayed carried out. Since tree diameter was themain predictor of carbon and biomass in the allometric equations selectedfor this investigation, it was measured. It employed to develop standcharacteristics in conjunction with total tree height. In order to toleratethe demands of use in challenging circumstances, fieldwork equipmentneeds to be accurate and long-lasting.

Field measurements of four carbon pools, such as aboveground biomass,belowground biomass, litterfall biomass, and soil, were used to collect theprimary data, including tree height and diameter, litterfall, and soilsamples. The equipment needed was determined by the type ofmeasurements (Pearson et al., 2005). In order to collect the data for thisstudy, the following instruments were used: meter tape to measure sampleplot distances; a clinometer to measure tree height; a caliper to measuretree diameter at breast height (DBH); an auger to collect sample soil;plastic bags to collect sample soil; a core sampler to sample soil for bulkdensity; and a Global Positioning System (GPS) to collect study sitecoordinates. Samples of bulk density up to a depth of 60 cm were collectedusing a core sampler.

2.3 Sample Size Determination

It is necessary to measure a sufficient number of sampling units to achievethe required level of precision, neither more nor less, in order toevaluation the biomass and carbon storage capacity of planted forests in astatistically and practically efficient manner (Thomas et al., 2015). Usingthe resources at hand, a practical method was taken to regulate thenecessary amount of sample schemes (Woldemariam, 2015). According toa general rule, the smaller the sample size needed for a planting, the moreprecise the measurement and the more changeable the material (Thomaset al., 2015). A total of 60 sample schemes were taken from C. lusitanicaand E. globulus tree species. The analysis suggested that thirty (30) sampleplots for a single, similar estate site were required. Based on this, a total of60 sample plots were taken from the study area (Picard et al., 2012).

2.3.1 Litterfall Sampling

The litterfall models stayed calm from sub-plot of 1 m x1 m in apiecescheme. At the corners and in the center of the main plot, five equal subquadrantsof 1 m2 each were established. Whole litterfall models in thesub-quadrants were composed by physical from each sub-quadrant. All ofthe litterfall inside the surround was calm and put in a flexible basket anda new mass was measured there with a digital balance. After weighed andcoded, all samples were evenly mixed per sample plot to prepare a total of60 composite samples (30 for each stand). From each plot 100 g of thecomposite samples were taken to the lab to determine the oven dry mass(Pearson et al., 2007). Then after the new mass grit, the model was takenfor laboratory analysis, which was taken into the Bahir Dar ForestDevelopment Office to estimate the oven-dry mass of the litterfall. Thedead wood at the study sites did not exist; hence, dead wood was notestimated in the current study.

2.3.2 Soil Sampling

For the purpose of analyzing bulk density and soil organic carbon, soilmodels stayed engaged from each sample plot. Three types of variablesmust be known in order to acquire an accurate inventory of soil organiccarbon stocks in mineral or organic soil: (1) depth; (2) bulk density(determined by calculating the oven-dried weight of soil from a knownvolume of tested material); and (3) the concentrations of organic carbonwithin the sample (Pearson et al., 2005). For bulk density determination,soil sample was placid from sub-samples which were designed at thecenter of apiece model design (1mx1m). The models stayed engaged fromquadrants 1mx1m (1m2) assigned in the four angles of the model schemesand from the one center of within (10 m x 10 m). After clearing away anyfallen debris, soil samples were taken from a depth of 60 cm in two depthclasses (0–30 and 30–60 cm) in order to determine SOC and bulk density,respectively.

Soil models stood obtained using a circle auger and a core sampler. Fiveequal weights of each sample from each quadrant were taken and mixedtogether homogeneously, and a composite subsample of 100 g of wetweight from each plot was given to the lab for analysis. Subsequently,using the Walkley-Black Procedure, the carbon fraction of each samplewas calculated in the laboratory (Walkley and Black, 1934). Finally, thebulk density, organic carbon, and soil organic carbon were computedusing this information. Also, using a core sampler that was carefullyinserted into the soil to prevent compaction, identical quadrants ofundistributed soil samples for bulk density determination were takenfrom the surface soil at the same time, from the center (Roshetko et al.,2002).

2.4 Methods of Data Analysis

2.4.1 Aboveground Biomass

Estimation of AGB, the allometric equivalences established by Berhe et al.(2013) for C. lusitanica (Eq. 1) and Debela (2017) for E. globulus (Eq. 2)were employed.

AGB = 0.0319* DBH1.8903 * H0.9194 (1)
AGB =0.479* DBH 2.2578 *H-0.374 (2)
AGBC = AGB × CF (3)

2.4.2 Below Ground Biomass and Carbon Storage Estimation

Based on the AGB each stand, BGB of the stand were assessed. BGB wasestimated by multiplying the above-ground biomass by 0.26, whichrepresents the root-shoot ratio (Hangarge et al., 2012).

BGB = 0.26× ABG (4)

Where, BGB= below-ground biomass and AGB=above-ground biomass, theconversion factor for AGB is 0.26.

BGBC= BGB ×CF (5)

2.4.3 Litter Biomass Carbon Storage Estimation

The litter samples were:

The carbon content of litter biomass was calculated by 0.37 of the dryweight of litter biomass per unit area (IPCC, 2006) and was estimatedusing the following formula:

CL = LB × 37 % (7)

2.4.4 SOC Estimation

According to Pearson et al. (2007), to establish a precise inventory of theorganic carbon stocks in the soil, the following parameters must beexamined: organic carbon concentrations, soil bulk density (derived froman oven-dry weight of soil from a known volume of measured material),and soil depth to which carbon is estimated for (0-60 cm) are all factors.The same sampling sub-quadrats advised for litter sampling were used toobtain samples for soil carbon analysis. The soil samples were air dried,thoroughly mixed, and sieved through a 2mm mesh size sieve before beingused for soil carbon measurement at the Amhara Design and SupervisionWorks Enterprise (ADSWE) center soil analysis of laboratory.

A cylindrical core sampler with a 5 cm diameter and 5 cm height was usedto collect a soil sample and determine the volume of the core for soil bulkdensity analysis. Each plot had a soil sample taken from it in a pit that was0-60 cm deep. So, using a metal soil corer sampler to dig the soil, two layersof 0–30 cm and 30–60 cm were chosen to be the most representative. The

freshly sampled soil obtained by bulk density cores was stored in a plasticbag, which was then labeled and sealed. The samples were then broughtto the lab oven for oven drying, where the oven dry weight was measured(re-weighted) after drying for 48 hours at a constant temperature of 105°Cto ascertain moisture content and bulk density (Pearson et al., 2005).Samples were tested using the Walkley method to determine thepercentage of organic carbon (Walkley and Black, 1934). The studysuggested method was used to calculate the soil’s carbon stock based onthe bulk density and carbon content of the soil at a set depth (Pearson etal., 2007). It is important to know the volume of the soil collected beforecomputing SOC because doing so aids in computing bulk density.

V=h*Πr2 (8)

Moreover, the bulk density of a soil sample was calculated as follows(Pearson et al., 2007).

BD = WavdryV(9)

The soil organic carbon stock pool was calculated using the formula(Pearson et al., 2005):

𝑆𝑂𝐶=𝐵𝐷∗𝑑∗%𝐶 (10)

2.5 Total Carbon Storage

The total carbon storage has been estimated:

CT=AGC+BGC+CL+ SOC (11)

2.6 Data Analysis

The numerical information (DBH, height, fresh and dried litter weights,and soil weights) gathered from the field was entered into a computer andset up on an Excel data sheet for future study. A one-way analysis ofvariance (ANOVA) was carried out to check for significant differences incarbon storage between both targeted tree species of forest biomass andsoil organic carbon across the two depth classes. When the analysis ofvariance (ANOVA) showed a significant difference among the differentfactors taken into consideration at (P< 0.05), a mean separation for eachtreatment was made by the Tukey least significant different comparisonmethod. All statistical tests were performed using Statistical Package forSocial Science (SPSS) version 27.1. Descriptive statistics were used tosummarize the frequency and density of each tree species in the sampleplot of the study area, the DBH and height of trees, and the mean, standarddeviation, minimum, and maximum value of carbon storage in differentcarbon pools of the study area, while one-way ANOVA and an independentT-test were used to determine the statistical significance of carbon storagevariation along selected tree species.

3. RESULTS AND DISCUSSION

3.1 Stand Characteristics for the Studied Species

This study showed that the mean value results of the tree biomassdetermining parameters such as height (H) and diameter at breast height(DBH). The total mean height (m) and DBH (cm) of E. globulus and C.lusitanica significantly differed at (P <0.05) (Table 1).

These results showed that the total mean height and DBH of Cupressuslusitanica was lower than Eucalyptus globulus (Table 1). This variation inspecies features, although it was planted in the same year and in the sameagro-ecological zones, could be the cause of this difference. Eucalyptus treecan reach heights of 40–55 m, and sometimes 70 m, with a diameter of 2m in Ethiopia (Bekele et al., 2007). while C. lusitanica trees can reachheights of up to 35 m in environments that are favorable for them (Orwaet al., 2009). But in this study area, the height of E. globulus reaches 60–70m, and the height of C. lusitanica was 35–45 m.

3.2 Aboveground Biomass and Carbon Storage Estimation

This result showed that the presence of species variation affects thecarbon storage of different pools in the forest stand (Fig.2). Theaboveground carbon content of the two species at the same age showed asignificant difference (p=0.001).

The ecosystems have significant potential variation in both short-term and long-term carbon storage (Houghton, 2005). This study showed, the variation in carbon storage among various tree species varied, and those that have been found to have high carbon storage. The current study results include assessing carbon stock at the targeted species and are crucial aimed at determining the function of forest ecologies in managing local and worldwide carbon emissions. From this finding, E. globulus stored a larger amount of Aboveground biomass carbon storage than C. lusitanica. This difference might be due to the presence of larger trees in DBH in E. globulus and difference in aboveground tree biomass estimator (allometric equation). According to Moges, (2011) the amount of carbon assessed in a particular forest is impacted by the various types of representations used for biomass assessment. Based on this AGB of species depends on their DBH; this could be due to the DBH difference between the two species that affects carbon storage. The other scenario could be the type of species difference. The older trees with a large DBH value will have a large AGB. As the age of trees increases, biomass also increases (Mamo, 2007). These are considered sources of variation in carbon storage (Tefera and Soromessa, 2015).

The current total mean AGC storage of C. lusitanica was significantly higher as compared to other studies AGC storage of (158±66.6 Mg C/ha) in the baseline study in Kenya (98.4±44.4 Mg C/ha) in Kenya, (39.05 t C/ha) in Wondo Genet, Ethiopia (128.1 t C/ha) in Addis Ababa (89.7 t C/ha) in the Gamo zone in southern Ethiopia and lower than (184.80 t C/ha) in Chilimo dry Afromontane forest, central Ethiopia and (180.14 Mg/ha) in ChatoAfromontane forest, western Ethiopia (Iticha, 2017; Omoro et al., 2013; Patula and Oeba, 2016; Yirdaw, 2018; Yilma and Derero, 2020;Mada et al., 2022; Tesfaye et al., 2020). E. globulus was significantly lower than (221±143.2 Mg C/ha) in the baseline study in Kenya (205.07 Mg/ha) in Chato Afromontane forest, western Ethiopia and (287.8 t C/ha) in Addis Ababa and higher than (67.5 t C/ha) in other study areas of Ethiopia and (59.3 t C/ha) in the Gamo zone in southern Ethiopia (Mada et al., 2022; Omoro et al., 2013; Iticha, 2017; Yilma and Derero, 2020; Tefera and Soromessa, 2015).

These variations may be the result of differences in tree ages, species height, and DBH range of trees, management practices, the environment, and the different allometric equations used. Another differences in structural parameters such as tree diameter, ecological area, topography (Yilma and Derero, 2020). Generally, the difference may be that the majority of Ethiopia’s plantation forests’ carbon storage are assessed using general allometric equations without taking into consideration the value of species-specific equations as a whole or the differences in species. However, applying a species-specific allometric equation to estimate carbon stocks produces better and more dependable results than a generic equation, particularly in less diversified plantation forests.

3.3 Belowground Biomass and Carbon Storage Estimation

From this finding, there stood a significant difference in belowground carbon content between the two tree species (P=0.001) Fig.3).

From these results belowground carbon stock (BGC) of E. globulus was higher than C. lusitanica. These study, compared with other studies, the over-all mean BGC storage of C. lusitanica was higher from (39±17.8 t C/ha) in the baseline study in Kenya, (10.15 t C/ha) in Wondo Genet, Ethiopia, (17.9 t C/ha) in the Gamo zone in southern Ethiopia, and lower than (43.23 Mg/ha) in Chato Afromontane forest, western Ethiopia, and (54.5 t C/ha) in Addis Ababa (Yilma and Derero, 2020; Omoro et al., 2013; Mada et al., 2022; Yirdaw, 2018; Iticha, 2017). The results of E. globulus was lower than (72±46.0 t C/ha) in the baseline study in Kenya, (122.5 t C/ha) in Addis Ababa and (49.22 Mg/ha), in Chato Afromontane forest, western Ethiopia and higher than (11.9 t C/ha) in the Gamo zone in southern Ethiopia (Mada et al., 2022; Omoro et al., 2013; Iticha, 2017;Yilma and Derero, 2020). The variation was due to the fact that, compared to the smallest trees, changes caused by larger trees have a greater capacity to produce larger amounts of below-ground biomass.

3.4 Litterfall Biomass and Carbon Storage Estimation

According to these findings, the biomass of litterfall was calculated using the carbon storage and total mean biomass of C. lusitanica and E. globulus. There was a significant difference noticed in the two tree species at the 95% confidence interval (P< 0.001) (Fig.4).

Compared to E. globulus, C. lusitanica had a slightly greater mean litter biomass carbon storage. This difference might be due to E. globulus litterfalls for fuel wood being easier to collect by the local community and the consumption of fuel where it is common in that area compared with C. lusitanica. The current study, compared with other studies, the litter carbon storage value of E. globulus was higher than (0.0031 t C/ha) in the Gamo zone in southern Ethiopia and lower than (3.25 Mg/ha) in the Chato Afromontane forest, Western Ethiopia (Iticha, 2017; Mada et al., 2022). The result of C. lusitanica was higher than (0.007 t C/ha) in southern Ethiopia and lower than (2.10 Mg/ha) in the Chato Afromontane forest, Western Ethiopia (Iticha, 2017; Yirdaw, 2018). From these different tree species and, thus, different forest types, come various amounts of litterfall value (Nave et al., 2022).

3.5 Soil Organic Carbon Stock (SOC)

From these results, the soil organic carbon storage of the two species was not showed significant difference (P=0.093) (Table 2).

The results for bulk density were in opposition to the concentration (%) and stock of soil organic carbon. This outcome was corroborated by(Sheikh and Tiwari, 2013). This indicates that the SOC dropped as soildepth rose. The buildup of dead and rotting logs and decomposed forest litter over the floor was the primary cause of this variation, which was caused by the presence of greater organic matter at the top soil layer. These differences were comparable to those discovered in a research by (Sheikh and Tiwari, 2013). A number of factors, including soil properties, forest management techniques, litter inputs, decomposition rates, and root turnover, influence soil organic carbon (Jandl et al., 2007). In the global carbon cycle and the forest’s carbon stores, soil organic carbon is essential (Sundarapandian et al., 2015). According to reports, C. lusitanica (86.1 kg/ha) and E. globulus (87 kg/ha) in Ethiopia are found within the range of soil organic carbon stock (0–40 cm) depth (Abate, 2004). This discrepancy could result from variations in planted forests, soil microorganisms’ rate of mineralization, climate, and soil type (Lal, 2004).

The low carbon content beneath C. lusitanica in the 0–60 cm soil depth in comparison to E. globulus suggests a poor inflow of fresh litter. According to this data, the top layer (0–30 cm) of both plantation stands had a much greater SOC than the lower layer (30–60 cm). The land use history of the

of the constant supply of litter, lower rate of disturbance, minimal impact of erosion, and lower temperature beneath the closed forest canopy, which may slow down decomposition and favour an extension of the residence time of soil organic matter, the upper layers have a higher SOC than the lower depth (Erskine et al., 2002). These findings showed that the soil at the study site contained a significant level of organic matter. The greater mean SOC stock may be the result of high SOM and quick litter breakdown, which maximizes carbon storage (Wolde et al., 2014).

3.6 Total Carbon Stock from Plantation Forests

The finding of this research showed that AGC, BGC and SOC of E. globulus species were higher than those of C. lusitanica and lower than LBC (Table 3).

The capability of plants to absorb CO2 through photosynthesis may be the reason for the discrepancy in this discovery, where there is more carbon storage in the forest. In accordance with the report of (Omoro et al., 2013) in Kenya, which claimed that the C. lusitanica woodland ecosystem had the biggest reservoir of soil organic carbon. Nevertheless, this outcome runs counter to Abate’s findings.

(2004), who discovered that the biomass of standing trees had a significant carbon content, but the soil had a tiny amount of organic carbon. Variations in the soil depth at which the data was gathered (0–30 cm) may be the cause of this little value. The variations are caused by how the forest is managed, and they could also be explained by the use of various allometric models for biomass measurement. Therefore, one of the main constraints resulting in significant changes in such estimates could be the reliance on allometric equations.

According to this study, forests that are maintained for greater carbon storage have the greatest potential for sequestering carbon and offer important mitigation alternatives. Nonetheless, research demonstrated that there were variations in species type and species-specific biomass allometric equations among the exotic species, which could be caused by the carbon content of aboveground, belowground, litterfall, and soil carbon storage. Differences in the allometric equation, tree species kinds, and the existence of greater diameter trees at breast height in E. globulus could all be contributing factors to the variation.

4.CONCLUSION AND RECOMMENDATIONS

This study uses DBH and total tree height as independent variables to determine the biomass and carbon store of the two targeted tree species. The outcome reveals which of the targeted tree species is most secluded. Compared to the C. lusitanica stand, the E. globulus stand had a much higher total mean carbon storage. Nonetheless, C. lusitanica’s litterfall carbon storage was marginally greater than E. globulus’s. According to the current finding, the total mean carbon stock of C. lusitanica was 312.36±97.88 t C/ha and E. globulus was 356.09±117.64 t C/ha. These findings suggest that species-to-species heterogeneity in carbon storage variation occurs. This study result indicates that, C. lusitanica and E. globulus are important in storing carbon stock in their biomass and soil. Finally, it could be concluded that E. globulus has stored more carbon than C. lusitanica. Hence it has a considerable role in mitigating the climate change by sequestrating carbon dioxide and to earn income from the current carbon marketing system in addition to its direct economic benefit plantation forests. Large-scale plantations of E. globulus and C. lusitanica have been established throughout Ethiopia, so comparable studies on carbon resources would be advantageous to both the local community and the nation. The development and implementation of allometric equations that are country-specific should be the main subject of future study. Knowledge of how this component affects the biomass and soil carbon pools for scientific advancement, more topographic aspect-related research will be required. The findings of this study indicate that more research will be required in this area because they do not address the role that plantations of the C. lusitanica and E. globulus species play in mitigating climate change. In addition to above-ground, below-ground, and soil carbon storage, fine roots can also hold carbon in plantation forests. So, in this study carbon storage in fine root was not included. Therefore, further study should concentrate on estimating the amount of carbon in this carbon pool.

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Resilience is often seen as a good thing; if an ecosystem or human society is resilient, it will be able to withstand the disruptions it faces; for a system to maintain a particular state, it must not be subjected to disturbances that exceed its ability to recover from that state, so resilience, like carrying capacity, is closely linked to sustainability; this is why it efforts to improve resilience from groups like the Resilience Alliance; they want our human-environment system to be preserved; city growth and sustainability depending on the stability of the urban indexes such as new development attracted by the people to economic prosperity, proximity as well as the political stability in the area; Keffi is being growing base on the urban growth indexes stated; sampling based on the indexing of the major factors of sustainability was conducted; about 95% of the inhabitant in the study area agreed upon educational expansion is responsible for urban sprawl as well as the city resilience, 80% on proximity index, 70% on economic stability, 60% social resilience and 45% on political stability of the inhabitant.

KEYWORDS: Urban city, Resilience Sustainability, Growth, Planning.

1. INTRODUCTION

Developing urban resilience is a salient achievement in the transformation of resource-based urban areas; this study assesses the impact of sustainable growth and conversion in resource-based cities on infrastructural growth, economic base, and urban ecology for social resilience; the results indicate that the sub-resilience illustrates an obvious upward movement; furthermore, the economic index for infrastructural social resilience has a spatial aggregation effect (Chai and Sun, 2023); transitions towards sustainable development can make tremendous achievements in ecological resilience in resource-based cities, but it can also negatively impact social resilience; therefore, governments need to address the social issues that can arise during transitions; this study provides a theoretical basis to inform government policy adjustments; regional disaster and risk produced by geomatics factors have risen in contemporary ages, posing enormous challenges to achieving the 2030 Global Sustainable Development Goals; urban resilience determines how cities develop, adapt, and recover from external shocks; recently, practical analysis and evaluation of urban resilience in South-East Asia are still lacking; some researchers developed the urban resilience index (URI) (Zeng et al., 2022; Amirzadeh et al., 2022); cities around the world face a range of threats, such as disasters and other disruptions; urbanization is a complex system, and inadequate resilience may hinder the growth and development of the subsystems, which results in major losses for the whole system (Lu et al., 2023); the vulnerabilities of cities are becoming bottlenecks that limit urban resilience and sustainable development in the face of disasters; urban resilience, which has gradually become a hot topic in urban research worldwide, refers to the ability of cities to withstand, absorb, adapt, and recover from the impact of risks (Sun et al., 2023); the goal of resilient urban development is to “make urban areas and human settlements safe, resilient, and sustainable” (UN, 2015); the term “resilience” originates from the Latin word “resilio”; the term has been applied in different fields of study, especially in environmental science, management and planning, sociology, and other disciplines (Smit et al., 2000); the present theories of urban resilience refer to the activities of complex urban systems to prevent, reuse, and recover from dangers in the environment; the world urbanization rate was 56% in 2021, and the proportion of people living in urban areas is expected to increase to 68% by 2050 (Bernstein, 2022); urbanization not only brings economic growth but also leads to diversification and its distribution, such as climate change, natural disasters, and social crises, which greatly impact the quality of life of urban residents (Meerow and Newell, 2021); urban growth is poised to constitute a threat to the security and sustainable development of cities (Serbanica and Constantin, 2023); the ability of an urban system or network to respond, adapt, and recover from these potential risks depends on the resilience of the city (Wu et al., 2023); urban resilience, which is related to urban planning and construction systems, has become a popular academic topic, and researchers have also defined this concept from many perspectives; especially in the field of climate change, resilience theory is considered one of the most effective methods to mitigate ecological problems, for example, the concept of combining urban resilience measurement.

2. METHODOLOGY

Study adopted an appropriate statistical analysis. These include the use of R2 from the regression analysis model. And the use of pie chart to illustrate the magnitude of the urban resilience in the study area.

3. RESULT AND DISCUSSION

The impact of urban resilience and stability index in this Study are based on the peripheral growth. These indexes include Social factor Economic factor Proximity factor Educational factor Political stability.

The two major indexes from the urban stability index in Keffi are the proximity to the Federal Capital Territory Abuja and the establishment of educational institution Nasarawa State University Keffi. As shown the chart in figure 1, other factors include socio-economic and political stability.

3. CONCLUSION

Two important factors of resilience based on this study have been identified: the proximity factor of the study area to Keffi to Abuja has 80% and the educational factors with 95% growth rate. Monitoring urban resilience is paramount in determining the rate at which urban green growth developed overtime. It was discovered that the introduction of new opportunities in term of urban education facilities. Urban resilience as so far identified other factors in the environment; these include socio-economic resilience, political and neighbourhoods’ peripheral growth. All of the new approaches of urban resilience discussed in other previous cities have being occurring in Keffi these days.

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This study accesses the quality of drinking water filtration plants in sector H-13, in Islamabad, Pakistan. The objective of this study focuses on the calculation of heavy metals pollution index in the samples collected for the analysis. The results are derived on the basis of physio-chemical and heavy metals parameters, a total 10 numbers of samples were tested for the analysis, to evaluate the quality and standard for the drinking water for human consumption. The physical parameters were studied using the portable instruments, chemical parameters were test using titration instruments, and Flame Atomic Absorption Model AA-7000 by Perkin Elmer were utilized to study the heavy metals parameters. Results shows that most of the samples for physical parameters are within the limit of WHO and PAK-EPA, except the sample TIR-6, which shows the high concentration of total dissolved solids, else the chemical parameters results shows that the concentration of total hardness and Mg ions were high than the prescribed limit in TIR-6 and TIR-9, heavy metals analysis shows that the pollution index is higher in 5 obtained samples, that were above the prescribed limit, for the cadmium, arsenic and lead. The calculation of Heavy metal pollution index indicates that majority of the filtration plants in the sector were highly subjected to contamination and are unsuitable for the consumption purposes.

KEYWORDS: World Health Organization, Pakistan Environmental Protection Agency, Heavy Metal Pollution Index.

1. INTRODUCTION

Water is vital in the provision of healthy lives and thus essential to mankind, but the presence of heavy metals is a high threat globally. Intake of drinking water that contain some metals like lead, cadmium, arsenic, chromium, and iron affects the human health by causing neurological, renal and cardiovascular complications (Alam, 2017). Heavy metal pollutants in Pakistan at the present time have become more common primarily because of urbanization, industrialization, and automobiles which are present in larger concentration in mega cities like Islamabad. Sector H-13 as a part of Islamabad has also seen different levels of heavy metal pollution in drinking water sources due to rapid urbanization and the increase of industries in the area and many residents are at risk (Treacy, 2019). Contamination from heavy metals in drinking water is also documented in many countries in the world. It was found that contamination is highly related to urban and industrial activities mainly because of improper waste disposal (Chaudhary et al., 2024).

A study that the groundwater of Karachi contains higher concentrations of lead and cadmium, so a continuous surveillance and treatment of water is required (Shakoor et al., 2015). Research work conducted in various cities of South Asia has also shown that HPI is useful in cumulative contamination because it gives a summative view of water quality (Siddique et al., 2023). The research was undertaken in DHA phase-II, Islamabad, to provide significant insights for water quality testing by using various factors and piper analysis to link water quality. The purpose of this research was to evaluate the performance of the sector’s filtration plants by administering various tests on water samples (Ahmed et al., 2023). A same suggested the filtration plants should utilize high-quality filter paper and regularly replace their membranes for their assessment conducted in Islamabad for the physio-chemical parameters (Ahmed et al., 2024).

The HPI method is applied for the assessment of heavy metal pollution in water resources because of its simplified procedure and inclusion of multiple indices of contamination. For instance, A group concluded that, this HPI method; are useful in pinpointing the areas with more heavy metals in order to enhance the application of relevant control strategies (Ahamad et al., 2020). The has study on heavy metal pollution in filtration plants revealed alarming concentrations of cadmium, chromium, lead, zinc, iron, manganese, and nickel. (Ahmed et al., 2024). These concentrations exceeded PAK-EPA standards, posing a significant risk to human health (Ahmed et al., 2023). Most of researcher also noted that while analysis of the water quality in Pakistan could elicit enthusiasm, it revealed that only a quarter of the population in that country has dependable access to safe drinking water (Hashmi et al., 2009). This study in particularly focuses on the assessment of the quality of drinking water with respect to heavy metal pollution, the mathematical model, used to explain various zones of contamination with regard to specific metal hazardous. Besides, thus, the primary and secondary inspection was also made to see the effects of all the studied pollutants in general. This study aims to highlight the heavy metals pollution index of the filtration plants in the sector, by testing different parameters. The main objective is to calculate the heavy metal pollution index for each sample tested.

1.1 Study Area

Geographically the area lies to the north-western edge of the city, the prevailing lithology and geological characteristics mark most of the sector as the recent deposits, samples collected from each source filtration plants, with proper marking of coordinates shown in Figure. 1.

2. METHODOLOGY

2.1 Sample Preservation and Storage

In the assessment of several factors in water samples three various bottles were used. The physical and chemical qualities of water were investigated in polyethylene bottles as recommended by most of researchers while analysis of heavy metals was in plastic bottles as described by( Muhammad et al., 2011;Khan et al., 2013).

The pH, TDS, and EC were analyzed on the field using portable instruments. In the lab other parameters were considered. To preserve heavy metals in the digested samples, 3 ml of concentrated nitric acid was added to each of the samples. For additional analysis, the samples were put into a cool place with a temperature of 4°C, and EC was analyzed on the field using portable instruments. In the lab, other parameters were considered (Chakrabarty and Sarma, 2011).

2.2 Sample Testing

After the calibration, the pH of the samples was then read using the portable pH meter. For stability, the samples were put in 250 ml beakers, and the readings were taken thrice for every sample. Due to the nature of atmospheric conditions on the samples, this was quantified in real time. Employing a conductivity probe for converting some conductivity readings into TDS values, TDS was determined in the water samples. Sample water was introduced into the meter in one milliliter; simultaneously with that, on the screen there appeared the readings. The Senso Direct 150 by LOVIBOND instrument was used during the analysis. Towards the determination of the samples’ total hardness, titration against EDTA as well as EBT indicators was employed. The flowing samples were prepared in a burette, diluted with distilled water, and later titrated with EDTA. To assess the mean value three readings were taken. The number of samples was changed and this method proceeded to samples. The chlorination process is used by all the filtration plants to eliminate bacteria. A measuring cylinder, burette, Erlenmeyer flask, funnel, and AgNO3 solution with potassium dichromate indicator were the equipment used in the chloride test. A LOVIBOND company meter was employed to read the samples for the determination of the EC. The electrode was immersed in a 250 ml beaker containing 200 ml sample, and the reading was taken on the screen. The UV visible spectrometric method was applied to the detection of sulfates in the samples. A sample of the quantity to be analyzed was prepared where 25 milliliters were taken in a flask. After the addition of 2 ml of sulfate buffer, mix well. Mortar and Pestle were used to fully mix the solution by adding 0.5g of Barium Chloride and allowed to stand for 1 hour. Subsequently, the solution was analyzed using a spectrometer of 420 nm wavelength. Atomic absorption spectroscopy is considered the best, as well as the most effective technique used in determining heavy metals in water. In this work, we employed the Perkin Elmer Atomic Absorption Model AA-7000 to analyze Cd, Pb, Fe, Mn, Ni, Cr, and Zn.

2.3 HPI Assessment

To facilitate the understanding of the results, HPI mathematical models are used in the paper. The Heavy Metal Pollution Index (HPI) is a method of rating the impact of definite individual heavy metals upon the total water quality. This technique was used to estimate the sources in the samples (Sheykhi and Moore, 2012). The calculation of HPI follows through the given equation (1) below:

Where, Wi is defined for the unit weight of the ith parameter, and according to equation (2), the formulation of the value is as follows Qi = ith parameter sub index while n depicts the total number of variables included as (Abou & Hafez, 2015).

The following formula provides the value of Qi, where K is the proportionality constant, which is normally set to 1, and Si is the standard value allowed for the ith parameter.

The HPI calculation is based on monitored value equations where Mi is the heavy metal of the ith parameter with the optimal value of Ii and the standard value of Si in ppb (μg/l) for the ith parameter. The values of Si and Ii were obtained from the source. As mentioned earlier all obtained results are in part per billion format for the HPI calculation (Nazari.,2014).

3. RESULTS

The samples tested for the various parameters suggest that the physical parameters are mostly suitable according to the standards suggested by PAK-EPA and WHO. The results indicated for pH show a range of min value obtained as 7.0 and max value obtained as 8.4, with mean average pH of 7.6 shown in table (1), for the concentration of total dissolved solids, the result indicates that most the samples lies within the prescribed range limit by both. The min value obtained for TDS is 308 mg/l and max value obtained is 615 mg/l, with mean average TDS of 433 mg/l in table (1), for the concentration of EC, the min value obtained is 392 μS/cm and max value obtained is 682 μS/cm, with an average EC of 539 μS/cm. The concentration for sample TIR-6 and TIR-9 was above the tolerated limits, recommending unsuitable filtration plant. The graphical representation of this table is shown in Figure 2 and Figure 3 are the IDW Maps of pH, TDS and EC indicating the high and low values of the physical parameters in the study area.

The samples tested for the various parameters, suggest that the chemical parameters are mostly suitable according to the standards suggested by PAK-EPA and WHO except few filtration plants. The results obtained for total hardness indicates min value of 145 mg/l, and max value of 292 mg/l, with mean average total hardness of 231 mg/l table (2), which shows good indicator as an average, but sample TIR-4 shows high hardness value, while TIR-6 and TIR-9 are above the prescribed limit set by both PAK-EPA and WHO. The results obtained for Ca2- ions indicates min value of 49 mg/l and max value of 158 mg/l, with mean average Ca2- ions concentration of 84 mg/l table (2), which is also the good indicator. For Mg ions concentration the min value is 16 mg/l and max value is 55 mg/l, with an average Mg concentration of 27.8 mg/l table (2). For Cl- ions, the min value is 18, with max value of 66, and average value for Cl- concentration of 36 mg/l table (2). For SO42- ions the min value is 12 mg/l and max value is 48 mg/l, with average SO42- concentration of 28.4 mg/l table (2). The result shows that except total hardness in few filtration plants are all in the range prescribed by the standard limit of both agencies. So the suitability for drinking water is good, except TIR-6 and TIR-9. The graphical representation of this table is shown in Fig. 4 and Fig. 6 are the IDW Maps of chemical parameters indicating their high and low concentrations in the study area. The piper analysis was also conducted for the available chemical parameters i.e. Ca2-, Mg, Cl- and SO42-.

The concentration of Cd table (3) varies from non-detectable in samples TIR-5, TIR-8, and TIR-10, to a maximum of 0.04 mg/l in TIR-6. While all samples remain within the WHO stringent limit of 0.05 mg/l, only TIR-6 reaches and slightly surpasses the Pak-EPA limit of 0.01 mg/l. The elevated levels in TIR-6 could be attributed to industrial or agricultural runoff in the sector. Iron levels range from a low of 0.01 mg/L in TIR-10 to a high of 0.2 mg/l in TIR-6, all well within the Pak-EPA and WHO limit of 0.3 mg/l. These values suggest that the water in this area is generally safe from iron contamination, which may otherwise affect taste and color. The variation may reflect natural mineral leaching from local geology, particularly in more iron-rich rocks. The high Fe concentration in TIR-6 could be due to regional geological composition or specific soil interactions. Lead concentrations across samples mostly fall under the WHO and Pak-EPA limit of 0.01 mg/l, with notable exceptions in TIR-4, TIR-5, TIR-6, and TIR-9 table (3). The highest recorded value, 0.05 mg/l in TIR-6, is significantly above the acceptable limits, posing potential health risks due to lead’s toxicity, especially affecting cognitive development in children. These elevated levels may stem from aging plumbing infrastructure, industrial discharge, or vehicular emissions, as lead can leach into water from pipes or be present in nearby soils and sediments. Nickel concentrations range from 0.001 mg/l in TIR-2 to a high of 0.1 mg/l in TIR-6 table (3). While most values stay within the WHO limit of 0.07 mg/l, the TIR-6 sample exceeds both the WHO and Pak-EPA limits. Nickel contamination may come from industrial sources. Chromium levels vary between non-detectable and 0.07 mg/l, with most values below the Pak-EPA and WHO limit of 0.05 mg/l. Samples such as TIR-6 and TIR-9 surpass this threshold table (3), which could indicate industrial pollution, as chromium is commonly used in metal plating, leather tanning, and dye production. High levels of Cr in drinking water can pose severe health risks due to its toxic and carcinogenic nature. Manganese levels are within safe limits in most samples, except for TIR-6 and TIR-9, where levels reach up to 0.5 mg/l and 0.8 mg/l, respectively table (3). These values surpass both WHO and Pak-EPA limits, indicating potential contamination from industrial effluents or natural leaching from manganese-rich minerals. Manganese, while an essential nutrient, can cause neurological issues in higher concentrations. Zinc concentrations are well below the permissible limit of 3 mg/L in WHO guidelines, with the highest concentration observed in TIR-6 at 0.7 mg/l table (3). Zinc is essential for health but can impart an undesirable taste to water in higher amounts. The elevated Zn in TIR-6 could stem from industrial sources. The graphical representation of this table is shown in Figure 7 and Fig. 8 are the IDW Maps of the heavy metal parameters indicating their high and low concentrations in the study area.

4. DISCUSSION

The physical parameters of the water samples collected from Sector H-13, Islamabad, reveal insights into pH, total dissolved solids (TDS), and electrical conductivity (EC) across ten locations. The pH values range from 7.0 to 8.4, comfortably within the permissible limits set by both the Pakistan Environmental Protection Agency (Pak-EPA) and WHO guidelines, indicating neutral to slightly alkaline conditions. TDS levels vary between 308 and 615 mg/l, with samples TIR-2, TIR-6, and TIR-9 exceeding the recommended threshold of 500 mg/l per WHO standards, suggesting potential mineral dissolution or contamination. EC measurements are within safe levels across all samples except TIR-6 and TIR-9, which approach or slightly exceed the WHO threshold of 600 μS/cm. These elevated TDS and EC values in TIR-6 and TIR-9 indicate a higher concentration of ions, possibly linked to anthropogenic or geological factors.
The chemical analysis of water samples from sector highlights variations in total hardness, calcium (Ca²⁺), magnesium (Mg), chloride (Cl⁻), and sulfate (SO₄²⁻) concentrations. Total hardness values range from 145 to 292 mg/L, with all samples within the Pak-EPA limit of 250 mg/l, though TIR-3, TIR-6, and TIR-9 show higher hardness levels. Calcium and magnesium concentrations are also well within permissible levels, suggesting balanced mineral content with no excessive hardness contributors. Chloride levels, between 18 and 66 mg/l, fall far below the maximum allowable concentrations, indicating limited chloride-based contamination sources. Sulfate concentrations are similarly low, ranging from 12 to 48 mg/l, comfortably below WHO lower threshold, indicating minimal industrial or agricultural influence. Overall, the results show that the water chemistry meets both national and international standards for safe drinking water, with some elevated hardness in certain samples possibly linked to geological factors rather than contamination.

Sample TIR-6 consistently exhibits the highest concentrations across several heavy metals, including Cd, Fe, Pb, Ni, Cr, Mn, and Zn, indicating a possible contamination hotspot. This pattern suggests potential localized pollution sources, perhaps an industrial site or runoff from agricultural activities using metal-based fertilizers. The general compliance with WHO and Pak-EPA guidelines in other samples suggests that water quality is mostly safe, though areas with specific industrial activities or infrastructure issues may still present localized risks. Elevated levels of lead, chromium, and nickel in samples like TIR-4, TIR-5, TIR-9, and TIR-6 highlight the need for targeted remediation and monitoring, as prolonged exposure to these contaminants can lead to significant health issues.

4.1 HPI Evaluation

Majority of the samples have Low HPI values and therefore all samples poses little danger in so far as the concentration of the heavy metals in these samples is concerned. The results of the tested samples TIR-6 are higher than the safe level and TIR-9 has higher contamination for certain type of metal. This analysis shows the possibility of pollution source within these areas, in limited geographical extent. The HPI evaluation was conducted using the same index formula to calculate the values of each metals and result was compiled using python coding, shown below;

4.2 Coding for calculating HPI

# Redefining the data and recalculating HPI after environment reset import pandas as pd
# Define the data for heavy metal concentrations in each sample
data = {
“Sample ID”: [“TIR-1”, “TIR-2”, “TIR-3”, “TIR-4”, “TIR-5”, “TIR-6”, “TIR-7”, “TIR-8″,”TIR-9”, “TIR-10”],
“Cd (mg/l)”: [0.005, 0.004, 0.006, 0.004, 0, 0.04, 0.01, 0, 0.01, 0],
“Fe (mg/l)”: [0.1, 0.09, 0.11, 0.12, 0.08, 0.2, 0.09, 0.1, 0.1, 0.01],
“Pb (mg/l)”: [0.002, 0.003, 0.001, 0.006, 0.009, 0.05, 0.008, 0.001, 0.02, 0.004],
“Ni (mg/l)”: [0.003, 0.001, 0.004, 0.008, 0.009, 0.1, 0.001, 0.006, 0.011, 0.004],
“Cr (mg/l)”: [0.01, 0.02, 0.015, 0.013, 0, 0.06, 0.0016, 0, 0.07, 0.011],
“Mn (mg/l)”: [0.023, 0.012, 0.027, 0.104, 0, 0.5, 0.019, 0.013, 0.8, 0.008],
“Zn (mg/l)”: [0.018, 0.016, 0.112, 0.019, 0.213, 0.7, 0.011, 0.008, 0.09, 0.38]
# Define permissible limits set by PAK-EPA for each metal
epa_limits = {
“Cd”: 0.01, “Fe”: 0.3, “Pb”: 0.01, “Ni”: 0.02, “Cr”: 0.05, “Mn”: 0.5, “Zn”: 5
# Calculate weightage Wi for each metal
weights = {metal: 1 / limit for metal, limit in epa_limits.items()
# Convert data to a DataFrame for easy manipulation
df = pd.DataFrame(data)
# Calculate Qi (Sub-index) for each sample and metal
for metal, limit in epa_limits.items():
df[f”Q_{metal}”] = (df[f”{metal} (mg/l)”] / limit) * 100
# Calculate Qi * Wi for each sample
for metal, weight in weights.items():
df[f”Qi_Wi_{metal}”] = df[f”Q_{metal}”] * weight
# Sum Qi*Wi and Wi for each sample to get HPI
df[“HPI”] = df[[f”Qi_Wi_{metal}” for metal in epa_limits]].sum(axis=1) / sum(weights.values())
# Add remarks based on HPI values
df[“Remarks”] = df[“HPI”].apply(lambda x: “Safe” if x < 100 else “Unsafe”)
# Select relevant columns to display
results = df[[“Sample ID”, “HPI”, “Remarks”]]
results

The graphical representation of the collective HPI value is shown in Figure 9 and Figure 10 is its IDW Map indicating its high and low value in the study area.

5. CONCLUSION

The comprehensive analysis of drinking water samples from Sector H-13, Islamabad, reveals that, while most samples are within the permissible limits set by both WHO and Pak-EPA standards, a few samples (notably TIR-6 and TIR-9) show elevated concentrations of heavy metals and some physical and chemical parameters. Physical parameters such as pH, TDS, and EC are largely suitable, with only slight exceed in specific samples. Chemical parameters also mostly align with regulatory limits, though total hardness in certain samples suggests the presence of mineral deposits possibly of geological origin.

The enhanced levels of Cd, Pb, Ni, Cr, Mn and Zn in samples TIR-6 and TIR-9 indicate localized pollution from industrial effluent or from the usage of metallic agricultural inputs or from near infrastructure. The HPI assessment reveals that although the majority of the samples have low risk, the high HPI of TIR-6 and TIR-9 require the focus of the health hazards to be prioritized and addressed. Hence, combining the results that show that currently the water quality in the Sector H-13 is fairly safe the positive contamination hot spots should underline the necessity of continuous water quality assessment and control measures in order to provide the community with safe drinking water.

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