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Carbon Sequestration by Native Tree Species around the Industrial Areas of Southern Punjab, Pakistan

Department of Forestry and Range Management, Bahauddin Zakariya University, Multan 66000, Pakistan
Department of Forestry & Range Management, Faculty of Agriculture, University of Agriculture, Faisalabad 38000, Pakistan
College of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, China
School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, China
MOE Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Farmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences, Xinxiang 453003, China
Authors to whom correspondence should be addressed.
Land 2022, 11(9), 1577;
Received: 9 August 2022 / Revised: 9 September 2022 / Accepted: 13 September 2022 / Published: 15 September 2022


Industries have been a major culprit in increasing carbonaceous emissions and major contributors to global warming over the past decades. Factories in the urban periphery tend to warm cities more as compared with rural surroundings. Recently, nature-based solutions have been promoted to provide solutions related to climate adaptations and mitigation issues and challenges. Among these solutions, urban trees have proven to be an effective solution to remove air pollutants and mitigate air pollution specifically caused by carbon emissions. This work was designed to assess the role of tree species in mitigating air emissions of carbon around the vicinity of various industrial sites. For this purpose, three different industrial sites (weaving, brick kiln, and cosmetic) were selected to collect data. Selected industrial sites were divided into two areas, i.e., (a) area inside the industry and (b) area outside the industry. The samples were collected from 100 square meters inside the industries and 100 square meters outside the industries. Five different trees species comprised of four replications were selected for sampling. About twenty trees species from inside and outside of the industries were measured, making it 120 trees from all three selected industries for estimating aboveground and belowground biomass, showing their carbon estimation. The results showed that Moringa oleifera depicted overall higher total biomass from both inside (2.58, 0.56, and 4.57 Mg ha−1) and outside sites from all three selected industries. In terms of total carbon stock and carbon sequestration inside the industry sites, Syzygium cumini had the most dominant values in the weaving industry (2.82 and 10.32 Mg ha−1) and brick kiln (3.78 and 13.5 Mg ha−1), while in the cosmetic industry sites, Eucalyptus camaldulensis depicted higher carbon, stock, and sequestration values (7.83 and 28.70 Mg ha−1). In comparison, the sites outside the industries’ vicinity depicted overall lower carbon, stock, and sequestration values. The most dominant tree inside came out to be Dalbergia sisso (0.97 and 3.54 Mg ha−1) in the weaving industry sites, having higher values of carbon stock and carbon sequestration. Moringa oliefra (1.26 and 4.63) depicted dominant values in brick kiln sites, while in the cosmetic industry, Vachellia nilotica (2.51 and 9.19 Mg ha−1) displayed maximum values as compared with other species. The findings regarding belowground biomass and carbon storage indicate that the amount of soil carbon decreased with the increase in depth; higher soil carbon stock values were depicted at a 0–20 cm depth inside and outside the industries. The study concludes that forest tree species present inside and outside the vicinity of various industries have strong potential in mitigating air emissions.

1. Introduction

The global rise of anthropogenic emissions of greenhouse gasses (GHG), especially carbon dioxide (CO2), for about a century now is increasing in rates that have never been observed before [1,2]. This unprecedentedly increasing concentration in CO2 in the post-industrial era could result in a rise in Earth temperatures from 2 °C to 6 °C by the end of the 21st century [2,3]. Generally, industries are the major culprit in increasing carbonaceous emissions and major contributors to global warming over the past decades [4]. Industries and factories installed in the urban periphery tend to warm the cities more than their rural surroundings [3]. Human activities such as increased factories, buildings, cars, and fewer trees enhance Urban Heat Islands (UHIs) [5]. The discharge of excessive amounts of pollutants from the industries of the developing countries warm the cities and bring severe damage to human cardiovascular and respiratory systems, thus accumulating hospital admissions and even premature mortality [6]. Studies regarding the air quality of Pakistan’s major cities have depicted a continuously deteriorating air quality due to unplanned industrial installments, high population, and vehicular discharge [6,7,8]. The urban centers of Pakistan, such as Lahore, Multan, and Rawalpindi, have excessive CO2, NOx, CO, and PM10 [7,9]. It has been depicted that increasing and unplanned industries and factories in the vicinity of an increasing population are a major source of emissions [10]. According to the World Bank, the particulate pollution originating from industrial and vehicular emissions has posed serious health concerns. The problem has claimed 700 deaths among children and a whopping 22,000 premature deaths in adults [11].
This increasing carbon footprint can be controlled by limiting emissions using methods such as putting taxes on industries with higher carbon emissions and gasoline, thus providing incentives for industries to pollute less and conserve energy [12]. Some governments have also invested in comprehensive climate solutions involving the usage of renewable energy, increasing fuel efficiency, a clean and green energy economy via applying carbon tax, and reducing tropical deforestation [13]. As the importance of ecosystems is being realized globally for the sustainability of human well-being, a recent concept termed a nature-based solution (NBS) is being promoted to provide solutions related to climate adaptations and mitigation issues and challenges [14]. This program uses healthy and functional ecosystems to make a cost-effective contribution in meeting the challenges of regulating air quality, climate regulation, prevention of soil erosion, and economic and social development [15]. According to the international union for conservation of nature (IUCN), nature-based solutions are termed actions that aim to protect, manage, and restore ecosystems [16].
Among the nature-based solutions (NBS), green infrastructure, i.e., urban trees, via various research has proven to be an effective solution to remove air pollutants [17]. Urban forests are well-reputed in positively contributing towards maintaining environment quality [18]. Throughout the literature, there is a normative assertion from most environmentalists that an increase in urban forests helps mitigate pollution problems in the urban centers, but it is more cost-effective than many other approaches [19,20]. Many scientific studies have described the trees’ carbon fixation capability, making them effective for ameliorating air quality by lessening CO2 concentration [21,22,23]. A recent study in the USA concluded that restoring an urban environment with an average level of tree cover can remove up to 27% of air pollutants through interception and absorption of particulate matter [24]. In various studies, it was observed that urban forests had the capacity to eliminate NO2 and O3 via absorbing and diffusing O3 via foliar gas exchange and dry deposition, thus decreasing the concentration of the pollutants in the atmosphere [25]. Nowak [23] observed trees in an urban scenario were absorbing pollutants from the air, thus decreasing air pollutants. Alonso et al. [26] depicted urban forests present in the pre-urban areas of Madrid, Spain to act as O3 sinks. Furthermore, the fiscal efficacy of this method was compared with any other conventional method of mitigating air pollution; this nature-based solution was considered cost-effective [22,27].
The role of urban forests in sequestering carbon around the avenues, streets, highways, and parks has been considerably studied [28]. However, to our best understanding, the role of urban forests and their function regarding the fixation of carbon dioxide released from industries in the Pakistani context are very little understood. The present study determines the best possible forest species to sequester carbon within or around the vicinity of various industrial sites. The objectives of this study in Multan city, Punjab, Pakistan were to quantify carbon sequestration by urban trees located in industry or factory campuses and then in the trees located outside in the vicinity of the industry in the range of 100 m2. Furthermore, it was aimed to determine the tree species with a higher ability to fix C into their biomass. The approach used by this study can be used to assess the actual and potential role of urban forests in reducing atmospheric CO2 in Pakistan.

2. Materials and Methods

2.1. Study Location and Sampling Methodology

This study was conducted in the semi-arid district of Multan (30.181459, 71.492157), Punjab, Pakistan, i.e., Multan city (Figure 1 Map). According to the Köppen–Geiger classification, the Multan district falls within the desert climate (BWh). The average annual precipitation is 175 mm, and the average annual temperature is 25.6 °C. Three different industrial sites (weaving, brick kiln, and cosmetic) were chosen for sampling and data collection.

2.1.1. Background Information on Selected Industries

All industries selected for the study had originally been situated quite far from the city. Rapid population spread and urban sprawl brought these industries in and around the periphery of the newly built housing societies around the city. A brick kiln, one of the selected sites for the study, was just present a few kilometers away from the city. It has a large school just next to its periphery. This made for the selection of this site to understand the role of urban greening at a local scale. The cosmetic industry part of the study site was another important selection, as recently a 50-acre mango orchard in its periphery was cleared to make way for a mega housing society. While the weaving industry was part of a rural setting away from the city, it was the biggest of the lot, surrounded by mango and orange orchards.

2.1.2. Sampling Methodology

For the collection of data, the selected industrial sites were divided into two areas, i.e., (a) area inside the industry and (b) area outside the industry. The area inside the industry constituted 100 m2. The sampled area included the industries’ campus boundaries and the areas adjacent to its outside walls. The inside area also included outside parts of the industry, including the boundary of the industry, which includes roads, streets, and canals. The area outside the industry also comprised 100 square meters. The trees and soil samples were taken 100 m away from the industries’ boundary walls. The sampling was conducted throughout the course of the 100 square meters in all four directions forming a square. All the industrial sites selected had native plant species present in and around their vicinity. The presence of mango orchards in all the selected study sites was common, as the area is famous for mango production. The selected tree species were dominant in the study sites, and hence were selected in the study. A total of 120 trees, 40 from each site was measured (Table 1).
The instrumental procedure for data collection includes the selection of trees in a 100 m radius. Each tree was then measured for height using a Suunto clinometer and girth using steel tape, whereas soil samples were extracted from different depths using soil auger.

2.2. Carbon Estimation of Above- and Belowground Biomass

For the collection of data, field visits were carried out in December 2019. Girth was measured at a height of 1.37 m from the ground level, and terminal height for each tree species within and outside the 100 square meters was measured and recorded. The tree biomass was calculated with an allometric equation from the literature (Table 2) and corrected for log bias where appropriate. The belowground biomass was assumed to be 26% of the aboveground biomass for all the tree species [29,30]. After that, individual tree biomass was converted into biomass per plot, biomass per hectare, and total carbon stock per hectare. Furthermore, we calculated the carbon content through biomass by assuming that the dry mass was 48.1% (0.48) carbon [31]. CO2 sequestration was calculated by multiplying tree carbon with a factor of 3.66 [32].

2.3. Soil Sampling and Analysis

Soil data were collected at two depths, 0–20 cm and 20–40 cm, near the base of the same tree species present within and outside the 100 square meters for each industry from the four cardinal directions. Soil sampling was conducted with the help of soil auger. Four samples were taken to make a composite sample. A total of 240 samples at both depths of 0–20 cm and 0–40 cm were collected and stored in polythene bags and analyzed at the Bahauddin Zakariya University, Multan. Bulk density was also measured by using a 100 cm3 stainless-steel cylinder. After air-drying the sample, samples were passed through a 2 mm sieve. Organic carbon was calculated using the Walkley–Black method. After that, the value of soil depth, bulk density, and percentage of organic carbon was multiplied to calculate the carbon of soil per hectare [37].
SOC = OC% × Bulk density (g cm−3) × sampling depth (cm)

2.4. Statistical Analysis

Descriptive statistics were performed by using Statistics 8.1 software (Statistical package). Furthermore, one-way ANOVA, including LSD (least significant difference between means), was also applied to test the difference across all study sites.

3. Results

3.1. Growth Parameters

The results regarding growth parameters: diameter (DBH cm) and height (m) of different trees inside and outside of all the selected industrial sites: weaving industry, brick kiln, and cosmetic are represented in Table 3 (p < 0.05). Overall, greater diameter and height were observed for trees inside the industrial sites than outside the industries. Inside the industrial area, the estimated range of tree height and diameter was 8.15 m–19.05 m and 19.82 cm–46.92 cm, while outside the range of growth parameters were 5.03 m–14.71 m and 11.93 cm–50.96 cm, respectively. In the weaving industry, the maximum tree height (12.27 m) was computed for Mangifera indica, while the maximum diameter (40.85 cm) was estimated for Moringa oleifera. Vachellia nilotica depicted a greater height (14.48 m and 14.71 m) than all other trees inside and outside the brick kiln industry. Maximum diameter (40.85 cm) inside the brick kiln industry was observed for Mangifera indica, while outside the industry for Moringa oleifera (38.42 cm), as shown in Table 3. The maximum height inside the cosmetic, industrial area was measured for Eucalyptus camaldulensis (19.05 m), while the maximum value (12.57 m) was computed outside of the industry was for Vachellia nilotica. However, a greater diameter (50.96 cm) was estimated for Mangifera indica outside the cosmetic, industrial area compared with other trees (Table 3).

3.2. Tree Biomass, Carbon Stock, and CO2 Sequestration Rate

The total mean biomass showed strong variation and was significantly (p ≤ 0.05) different among tree species present inside and outside the selected industries: weaving, brick kiln and cosmetic industry (Table 4). Overall, aboveground, belowground, and total biomass were greater for trees present inside the industrial area than outside. Inside the industrial area, the maximum total biomasses were, 2.58 Mg ha−1 (weaving), 2.74 Mg ha−1 (brick kiln), and 16.32 Mg ha−1 (cosmetic) for Moringa oleifera, Syzygium cumini, and Eucalyptus camaldulensis, respectively, while outside the industrial area the range of total biomass was (0.09 Mg ha−1–5.22 Mg ha−1) across all industries. Outside the weaving industry, the maximum total biomass was computed for Dalbergia sissoo (2.02 Mg ha−1), while the minimum for Moringa oleifera (0.09 Mg ha−1). Similarly, outside of the brick kiln and cosmetic industries, higher biomass was estimated for Moringa oleifera and Vachellia nilotica, whereas minimum total biomass was measured for Mangifera indica (0.19 Mg ha−1 and 0.27 Mg ha−1) as depicted in Table 4.
Moreover, AGB (aboveground biomass), BGB (belowground biomass), and TB (total biomass) represent the aboveground biomass, belowground biomass, and total biomass, respectively. Like tree biomass, strong variation in tree carbon stock and CO2 sequestration was observed among various tree species inside and outside the selected industrial sites. The measured above, below, and total carbon stock of all tree species was significantly different (p ≤ 0.05), as depicted in Table 3. Inside the weaving and brick kiln industries, the maximum total carbon stock (2.82 Mg ha−1 and 3.78 Mg ha−1) was measured for Syzygium cumini; however, for the cosmetic industry, the maximum carbon stock (7.83 Mg ha−1) was estimated for Eucalyptus camaldulensis (Table 3). Vachellia nilotica trees present outside the cosmetic industry exhibited greater carbon stock (2.51 Mg ha−1) than trees present outside the weaving and brick kiln industries. Overall, maximum CO2 sequestration (10.32 Mg ha−1 yr−1 (weaving), 13.85 Mg ha−1 yr−1 (brick Kiln), and 28.70 Mg ha−1 yr−1 (cosmetic) was measured for trees present inside the selected industries as compared with outside, as described in Table 5.

3.3. Soil Carbon stock

The organic carbon (OC%) and soil carbon stock (SOC Mg ha−1) decreased for all tree species with the increase in depth for both inside and outside of the selected industrial sites: 0–20 cm > 20–40 cm (Table 4). It was observed that overall, from both inside and outside sites, Dalbergia sissoo had the maximum SOC: 53.05 Mg ha−1, 66.16 Mg ha−1 (0–20 cm), 43.11 Mg ha−1, and 47.51 Mg ha−1 (20–40 cm). Vachellia nilotica, in the weaving and cosmetic industry sites, displayed the max OC (2.31%, 2.11%, and 1.9%, 1.86%) and SOC (48.16 Mg ha−1, 43.08 Mg ha−1, 40.53 Mg ha−1, and 38.95 Mg ha−1) at 0–20 cm and 20–40 cm depths, while Moringa oleifera showed the least values at these sites. Similarly, outside the weaving and cosmetic industry, maximum OC and SOC for both depths was measured for Vachellia nilotica and minimum OC: 3.08% and 2.19% (0–20 cm depth) and 2.04% and 1.95% (20–40 cm depth) and SOC: 63.16 Mg ha−1 and 47.07 Mg ha−1 (0–20 cm depth) and 42.31 Mg ha−1 and 40.78 Mg ha−1 (20–40 cm depth) for Moringa oleifera, as described in Table 6’s first bullet.

4. Discussion

The present study aimed to assess the capability of forest tree species in the land around and inside the industrial sites in Pakistan for regulation and sequestering of carbon to mitigate CO2 emissions within the surroundings of various industrial locations. In Pakistan, around most industrial sites, many barren lands and conventional agriculture-practicing farms can be invested in as a nature-based solutions for mitigating direct CO2 emissions. The current study emphasized that the species have an immense capacity to sequester carbon generated from industrial surroundings. The findings also point out their tolerance to higher amounts of CO2 and their capacity to intercept pollutants, thus improving the urban air quality. A total of three industrial sites were analyzed in this particular research to determine carbon sequestration in the trees and soil present inside the 100 m2 zone of the industries and the 100 m2 outside of the industrial sites.
Biomass accumulation and growth depend upon site quality and conditions, type of soil on which trees are planted, age, management practices, and their interaction with belowground components [37,38]. In the present study, the maximum growth and biomass accumulation (above- and belowground) was measured for tree species present inside the selected industrial sites compared with outside. The species inside the industry that depicted the maximum above- and belowground biomass was M. oleifera (2.24 and 0.58 Mg ha−1), followed by Syzygium cumini (2.17 and 0.57 Mg ha−1), and Eucalyptus camaldulensis (12.95 and 3.36 Mg ha−1), whereas outside the industrial area, Vachellia nilotica and D. sissoo trees displayed higher growth and biomass accumulation. The above- and belowground biomass of different tree species of the present study are comparatively lower than the biomass measured by Yasin et al. [39] for P. deltoides, Yasin, et al. [40,41] for D. sissoo, B. ceiba, P. deltoides, and E. camaldulensis, Yasin, et al. [39] for V. nilotica in Pakistan, Kanime et al. [42] for D. sissoo and P. deltoids, and Faiz, et al. [43] for a eucalyptus hybrid plantation in the Tarai region of India. However, a similar amount of total biomass accumulation has been documented in previous studies in E. camaldulensis [44], A. nilotica, and D. sissoo [45].
Carbon stock is the absolute amount of carbon present at the time of inventory, while carbon sequestration rate refers to the procedure of removing carbon from the atmosphere and dumping it in a carbon pool [46]. Trees, being perennial vegetation, can store an ample amount of carbon both in above- and belowground parts. In the present study, above- and belowground carbon, along with CO2 sequestration rate, was estimated inside and outside different industries. The total tree carbon stock and sequestration rate inside the industrial area was higher for E. camaldulensis (7.83 Mg ha−1 and 28.70 Mg ha−1), while in the outside industries, higher carbon stock and sequestration were measured for V. nilotica (2.51 Mg ha−1 and 9.19 Mg ha−1). The amount of carbon measured in the present study is comparatively much less as compared with the findings of Arora and Chaudhry [47] for V. nilotica + D. sissoo (41.44 t ha−1) and Zabek and Prescott [48] for P. deltoids (51.2 t ha−1) planted in different sub-continent regions. However, our findings are in agreement with the results of Yasin et al. [41], who reported total carbon stock (7.17 t ha−1) in 8-year-old V. nilotica trees. Similarly, the CO2 sequestration is slightly higher than that estimated by Kaul et al. [49] for P. deltoids (8 t ha−1 yr −1) and Lal and Singh [50] for plantations (3.2 t ha−1 yr−1). The carbon sequestering potential of forest tree species in the current study is relevant to research conducted in the USA that depicted the restored lands with forest species around industrial sites as capable of mitigating the emissions generated by them [24].
In a terrestrial ecosystem, the soil is considered an important carbon pool to mitigate atmospheric CO2. Soil carbon stock depends on the land use pattern, soil type, topography, climatic conditions, and management practices [41,51]. Greater soil carbon is estimated in soils with trees due to more leaf litter [46]. The findings of our study indicated that the amount of soil carbon decreased with the increase in depth: higher soil carbon stock was estimated at 0–20 cm depth both inside and outside of the industries. The fact behind this greater amount of soil carbon in surface soil is due to the higher accumulation of tree litter, which ultimately results in greater carbon input [44,51,52]. Our findings are very consistent with the findings of Arora and Chaudhry [45] for V. nilotica and D. sissoo and Arora et al. (2014) for P. deltoids trees. Overall, inside and outside the industries, maximum SOC was: 53.05 Mg ha−1, 66.16 Mg ha−1 (0–20 cm), 43.11 Mg ha−1, and 47.51 Mg ha−1 (20–40 cm) for D. sissoo. The above values are comparatively higher than those reported by Yasin et al. [41] in 8-year-old V. nilotica trees (26.27 Mg ha−1) at 0–15 cm depth. However, our findings are very consistent with the findings of Yasin et al. [39], who reported soil carbon stock of 38.57 Mg ha−1, 41.81 Mg ha−1, and 43.73 Mg ha−1 at 0–30 cm depth in 6-year-old P. deltoids trees across three tehsils of the Chiniot district.

5. Policy Implications and Conclusions

Increasing global warming induced by industries is an eminent source of increasing disasters and the spread of new diseases. The developing world has put very little effort into providing their industries with clean and green emissions. The most common, cheap, and less laborious strategy of mitigating industrial emissions are nature-based solutions. In support of various previous research, the current study has proven nature-based solutions to mitigate carbon emissions. The current findings depict that tree species present inside and outside the vicinity of various industries have strong potential in mitigating air emissions.
The national government is actively participating in forming industrial and economic zones in the country that could, later on, increase pollution levels. The current study recommends that plantation of native and fast-growing trees in and around these zones be made mandatory. This study indicates a huge potential of natural-based solutions on a national level to mitigate the direct air pollution emissions from the industrial sites in the suburbs of big cities and make them locally sustainable. Furthermore, it recommends the government and industrial sector rely on nature-based green solutions and employ greener technology to ensure amplified mitigation of carbon emissions.
The current study acts as a starting point for the industrial sector to explore the viability and practicability of applying nature and technology-based solutions to mitigate emissions. Additional and more focused work on this aspect can lead the industrial community in the region to use these studies as a foundation for identifying facilities in and around the vicinity of industries for the growth of vegetation for mitigating emissions.

Author Contributions

M.Z., G.Y. and A.J.: Conceptualization, Formal analysis, Writing—Original Draft Preparation. S.K.Q., A.U.H. and M.Y.: Resources, data curation, Field work, Methodology. S.U.R. and W.G.: Writing—Review and Editing, Visualization, Validation and Funding Acquisition. All authors have read and agreed to the published version of the manuscript.


The National Natural Science Foundation of China (No. 51709265).

Institutional Review Board Statement

The study did not require ethical approval, so chose to exclude this statement.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data sets are associated with the paper. Moreover, an excel sheet for raw data will be provided on request after acceptance of the paper.

Conflicts of Interest

On behalf of the first author and remaining co-authors, I declare that no one has any conflict of interest.


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Figure 1. Map of the study site.
Figure 1. Map of the study site.
Land 11 01577 g001
Table 1. Different trees species present in different industries.
Table 1. Different trees species present in different industries.
Weaving IndustryBrick Kiln IndustryCosmetic Industry
1V. niloticaV. niloticaD. sissooD. sissooE. camaldulensisE. camaldulensis
2M. indicaM. indicaV. niloticaV. niloticaD. sissooD. sissoo
3D. sissooD. sissooM. oleiferaM. oleiferaM. oleiferaM. oleifera
4M. oleiferaM. oleiferaM. indicaM. indicaM. indicaM. indica
5S. cuminiS. cuminiS. cuminiS. cuminiV. niloticaV. nilotica
Table 2. Different equations used to calculate the aboveground biomass of different tree species. Equations are presented transformed appropriately to calculate component biomass in kg directly. In the absence of belowground equations for a species, belowground biomass was assumed to be 26% of aboveground biomass [29].
Table 2. Different equations used to calculate the aboveground biomass of different tree species. Equations are presented transformed appropriately to calculate component biomass in kg directly. In the absence of belowground equations for a species, belowground biomass was assumed to be 26% of aboveground biomass [29].
Vachellia nilotica10−1.0646 ∗ D2H0.9098Ravindranath and Ostwald [33]
Mangifera indica2.87 ∗ D0.87
Dalbergia sissooe−3.1141 ∗ D2H0.9719Brown, et al. [34]
Moringa oleiferae−3.1141 ∗ D2H0.9719Brown, et al. [34]
Syzygium cumini10−1.2066 ∗ D2H0.9872Rai [35]
Eucalyptus camaldulensise−2.2660 ∗ D2.4663Hawkins [36]
D = Diameter (cm) and H = Height (m).
Table 3. Height (m) and diameter (cm) of different tree species present inside and outside the different industries. Values were reported as mean ± standard deviation.
Table 3. Height (m) and diameter (cm) of different tree species present inside and outside the different industries. Values were reported as mean ± standard deviation.
Height (m)Diameter (cm)Estimated Age ClassHeight (m)Diameter (cm)Estimated Age Class
WeavingDalbergia sissoo8.99 ± 1.4620.02 ± 5.428–139.53 ± 1.9223.05 ± 8.509–14
Mangifera indica12.27 ± 1.1238.01 ± 17.708–139.14 ± 2.1640.24 ± 9.9410–15
Moringa oleifera12.19 ± 1.4240.85 ± 14.665–105.03 ± 0.8111.93 ± 0.771–5
Syzygium cumini10.97 ± 1.4925.74 ± 15.428–139.75 ± 3.5124.87 ± 10.007–12
Vachellia nilotica9.98 ± 1.1032.76 ± 3.657–1111.96 ± 2.9825.48 ± 8.375–10
Brick kilnDalbergia sissoo11.51 ± 1.8721.64 ± 4.8711–169.98 ± 1.4512.34 ± 4.913–7
Mangifera indica11.28 ± 3.6440.85 ± 9.925–1010.82 ± 1.1333.57 ± 12.783–7
Moringa oleifera9.22 ± 1.8020.22 ± 6.601–511.74 ± 1.5038.42 ± 23.497–13
Syzygium cumini12.80 ± 2.6927.50 ± 17.768–139.60 ± 3.7818.61 ± 12.615–10
Vachellia nilotica14.48 ± 1.6027.91 ± 12.984–914.71 ± 4.5123.46 ± 7.293–7
CosmeticDalbergia sissoo8.76 ± 1.2222.44 ± 9.8410–157.47 ± 2.7321.64 ± 12.137–12
Eucalyptus camaldulensis19.05 ± 1.5833.57 ± 9.985–108.38 ± 1.3518.40 ± 2.591–5
Mangifera indica11.89 ± 3.8543.89 ± 18.026–1212.19 ± 3.5950.96 ± 18.058–14
Moringa oleifera12.34 ± 3.0846.92 ± 21.807–139.37 ± 1.7137.61 ± 19.695–10
Vachellia nilotica8.15 ± 2.2719.82 ± 9.673–712.57 ± 4.2239.64 ± 14.6710–15
Table 4. Biomass of different tree species present inside and outside the different industries.
Table 4. Biomass of different tree species present inside and outside the different industries.
AGB Mg ha−1BGB Mg ha−1TB Mg ha−1AGB Mg ha−1BGB Mg ha−1TB Mg ha−1
WeavingDalbergia sissoo0.38 b ± 0.160.09 b ± 0.040.48 b ± 0.211.60 a ± 1.210.42 a ± 0.312.02 a ± 1.52
Mangifera indica0.17 b ± 0.070.04 a ± 0.010.20 b ± 0.090.18 b ± 0.040.05 b ± 0.010.22 b ± 0.05
Moringa oleifera2.24 a ± 1.420.58 a ± 0.372.58 b ± 1.790.07 b ± 0.020.02 b ± 0.000.09 b ± 0.02
Syzygium cumini1.62 a ± 0.630.42 b ± 0.162.04 a ± 0.791.61 ab ± 0.860.30 ab ± 0.221.46 ab ± 1.08
Vachellia nilotica1.89 a ± 0.560.49 a ± 0.152.38 a ± 0.701.08 ab ± 0.830.28 ab ± 0.221.36 ab ± 1.05
Brick KilnDalbergia sissoo0.57 ab ± 0.240.15 ab ± 0.060.72 ab ± 0.310.17 b ± 0.120.04 b ± 0.030.21 b ± 0.15
Mangifera indica0.18 b ± 0.040.05 b ± 0.010.22 b ± 0.050.14 b ± 0.050.03 b ± 0.010.19 b ± 0.06
Moringa oleifera0.44 ab ± 0.330.11 ab ± 0.860.56 ab ± 0.422.09 a ± 2.160.54 a ± 0.562.64 a ± 2.73
Syzygium cumini2.17 a ± 2.440.57 a ± 0.632.74 a ± 3.070.84 ab ± 0.900.22 ab ± 0.231.06 ab ± 1.13
Vachellia nilotica2.09 a ± 1.340.54 a ± 0.352.64 a ± 1.701.35 ab ± 0.740.35 ab ± 0.191.70 ab ± 0.92
CosmeticDalbergia sissoo0.55 b ± 0.430.14 b ± 0.110.69 b ± 0.540.56 b ± 0.790.14 b ± 0.200.71 b ± 0.99
Eucalyptus camueldenisis12.95 a ± 5.363.36 a ± 1.3916.32 a ± 6.761.04 b± 0.410.27 b ± 0.101.31 b ± 0.52
Mangifera indica0.18 b ± 0.060.04 b ± 0.010.23 b ± 0.080.21 b ± 0.060.05 b ± 0.010.27 b ± 0.08
Moringa oleifera3.63 b ± 3.10.94 b ± 0.824.57 b ± 4.021.64 b ± 1.520.42 b ± 0.392.06 b ± 1.92
Vachellia nilotica0.76 b ± 0.790.19 b ± 0.200.96 b ± 1.004.15 a ± 3.061.07 a ± 0.795.22 a ± 3.85
Means with similar letters are not statiscally different at p 0.05.
Table 5. Carbon of different tree species present inside and outside the different industries.
Table 5. Carbon of different tree species present inside and outside the different industries.
Industries SpeciesInsideOutside
AGC Mg ha−1BGC Mg ha−1TC Mg ha−1CO2 Sequestration Mg ha−1AGC Mg ha−1BGC Mg ha−1TC Mg ha−1CO2 Sequestration Mg ha−1
WeavingDalbergia sissoo0.18 b ± 0.080.05 b ± 0.020.23 cd ± 0.100.83 cd ± 0.360.77 a ± 0.580.20 a ± 0.150.97 a ± 0.723.54 a ± 2.67
Mangifera indica0.08 b ± 0.030.02 b ± 0.010.10 d ± 0.040.36 d ± 0.150.08 b ± 0.020.02 b ± 0.000.10 b ± 0.020.38 b ± 0.08
Moringa oleifera1.08 a ± 0.690.28 a ± 0.181.08 bc ± 0.693.95 bc ± 2.500.03 b ± 0.010.01 b ± 0.000.04 b ± 0.010.15 b ± 0.03
Syzygium cumini0.78 a ± 0.300.20 a ± 0.082.82 a ± 1.1010.32 a ± 4.030.56 ab ± 0.410.14 ab ± 0.110.70 ab ± 0.522.57 ab ± 1.89
Vachellia nilotica0.91 a ± 0.270.24 a ± 0.071.14 b ± 0.344.19 b ± 1.240.52 ab ± 0.400.13 ab ± 0.100.65 ab ± 0.512.38 ab ± 1.85
Brick KilnDalbergia sissoo0.27 ab ± 0.120.07 ab ± 0.030.34 ab ± 0.151.25 b ± 0.540.08 b ± 0.060.02 b ± 0.020.10 b ± 0.070.37 b ± 0.26
Mangifera indica0.09 b ± 0.020.02 b ± 0.000.10 b ± 0.020.39 b ± 0.080.07 b ± 0.020.01 b ± 0.010.09 b ± 0.030.33 b ± 0.11
Moringa oleifera0.21 ab ± 0.160.05 ab± 0.040.26 ab ± 0.200.98 b ± 0.731.00 a ± 1.040.26 a ± 0.271.26 a ± 1.314.63 a ± 4.80
Syzygium cumini1.04 a ± 1.161.32 a ± 1.473.78 a ± 4.2413.85 a ± 15.530.40 ab ± 0.430.10 ab ± 0.110.50 ab ± 0.541.85 ab ± 1.99
Vachellia nilotica1.00 a ± 0.651.27 a ± 0.810.05 a ± 0.048.31 ab ± 5.350.65 ab ± 0.350.16 ab ± 0.090.82 ab ± 0.442.99 ab ± 1.62
CosmeticDalbergia sissoo0.26 b ± 0.200.06 b ± 0.050.33 b ± 0.261.23 b ± 0.950.27 b ± 0.370.07 b ± 0.090.34 a ± 0.471.25 b ± 1.75
Eucalyptus camaldulensis6.22 a ± 2.571.61 a ± 0.677.83 a ± 3.2428.70 a ± 11.890.50 b ± 0.200.13 b ± 0.050.63 b ± 0.252.30 b ± 0.92
Mangifera indica0.09 b ± 0.030.02 b ± 0.010.11 b ± 0.040.41 b ± 0,150.10 b ± 0.030.02 b ± 0.010.13 b ± 0.040.47 b ± 0.18
Moringa oleifera1.74 b ± 1.530.45 b ± 0.392.19 b ± 1.938.04 b ± 7.070.78 b ± 0.730.20 b ± 0.190.99 b ± 0.923.63 b ± 3.37
Vachellia nilotica0.36 b ± 0.380.09 b ± 0.380.46 b ± 0.481.70 b ± 1.761.99 a ± 1.460.51 a ± 0.382.51 a ± 1.859.19 a ± 6.78
Means with similar letters are not statiscally different at p 0.05. Where first and second values represent mean and standard deviation. Moreover, AGC (aboveground carbon), BGC (belowground carbon).
Table 6. Soil organic carbon of different tree species present inside and outside the different industries. Where first and second values represent the mean values and standard deviation. Moreover, OC (organic carbon) and SOC (soil organic carbon) represent organic carbon and soil organic carbon.
Table 6. Soil organic carbon of different tree species present inside and outside the different industries. Where first and second values represent the mean values and standard deviation. Moreover, OC (organic carbon) and SOC (soil organic carbon) represent organic carbon and soil organic carbon.
SOC Mg ha−1
SOC Mg ha−1
TOC (0–40) (%)TSOC Mg ha−1 (0–40)OC%
SOC Mg ha−1
SOC Mg ha−1
TOC (0–40)
TSOC Mg ha−1 (0–40)
WeavingDalbergia sissoo1.90 b ± 0.0740.81 b ± 3.321.57 b ± 0.0234.73 b ± 2.012.685 ± 0.04558.175 ± 4.325 2.64 b ± 0.0456.72 b ± 2.531.78 b ± 0.0638.62 b ± 2.153.53 ± 0.0776.03 ± 3.605
Mangifera indica1.49 d ± 0.0329.43 d ± 1.111.32 d ± 0.0227.17 d ± 0.382.15 ± 0.0443.015 ± 1.32.41 d ± 0.0347.45 d ± 1.251.65 a ± 0.0533.31 c ± 1.163.23 ± 0.05564.10 ± 1.83
Moringa oleifera1.39 d ± 0.0728.05 d ± 0.631.2 e ± 0.0424.69 e ± 0.831.295 ± 0.09 26.37 ± 0.731.94 e ± 0.0538.20 e ± 2.531.39 c ± 0.0328.54 d ± 0.771.66 ± 0.06552.47 ± 2.91
Syzygium cumini1.75 c ± 0.0835.53 c ± 2.381.46 c ± 0.0730.46 c ± 0.872.48 ± 0.11532.9 ± 2.8152.51 c ± 0.0552.19 c ± 1.381.70 b ± 0.0135.21 c ± 1.193.36 ± 0.0569.75 ± 1.97
Vachellia nilotica2.31 a ± 0.0748.16 a ± 2.551.9 a ± 0.0140.53 a ± 1.233.26 ± 0.07568.42 ± 3.163.08 a ± 0.1163.16 a ± 2.862.04 a ± 0.1442.31 a ± 3.754.1 ± 0.1884.31 ± 4.73
Brick kilnDalbergia sissoo2.6 a ± 0.1053.05 a ± 2.862.01 a ± 0.0643.11 a ± 0.733.605 ± 0.0874.60 ± 3.223.11 a ± 0.1066.16 a ± 2.402.20 a ± 0.1447.51 a ± 2.684.21 ± 0.1789.915 ± 3.74
Mangifera indica1.51 d ± 0.1231.38 d ± 2.261.32 b ± 0.0428.19 c ± 1.932.17 ± 0.1445.47 ± 3.222.01 e ± 0.0541.54 e ± 1.981.72 c ± 0.0536.12 d ± 1.382.87 ± 0.07559.66 ± 1.68
Moringa oleifera2.31 b ± 0.0749.69 a ± 1.781.91 b ± 0.0542.57 a ± 0.783.26 ± 0.0970.97 ± 2.172.88 b ± 0.0861.99 b ± 4.292.00 b ± 0.0744.08 b ± 2.103.88 ± 0.1584.03 ± 6.39
Syzygium cumini1.93 c ± 0.0340.40 b ± 0.151.72 c ± 0.0236.82 b ± 1.052.79 ± 0.0458.81 ± 0.672.72 c ± 0.0555.76 c ± 2.491.91 b ± 0.0239.26 c ± 0.943.675 ± 0.0775.39 ± 2.96
Vachellia nilotica1.83 c ± 0.1936.80 c ± 3.271.43 d ± 0.0230.03 c ± 1.302.54 ± 0.2151.8 ± 3.922.45 d ± 0.29549.51 d ± 0.591.95 b ± 0.0540.73 c ± 1.533.42 ± 0.1769.87 ± 1.35
CosmeticDalbergia sissoo1.85 b ± 0.0636.49 b ± 1.151.72 c ± 0.0535.39 b ± 1.412.71 ± 0.1154.18 ± 2.562.02 c ± 0.0539.74 b ± 1.151.36 c ± 0.0537.69 b ± 1.252.27 ± 0.07558.58 ± 1.775
Eucalyptus camaldulensis2.06 a ± 0.0442.78 a ± 1.231.8 b ± 0.0238.07 a ± 1.322.96 ± 0.0562.18 ± 1.892.19 b ± 0.0345.56 a ± 2.061.89 a ± 0.0640.01 ab ± 1.583.13 ± 0.0665.5 ± 2.84
Mangifera indica1.46 c ± 0.0629.42 c ± 2.251.39 d ± 0.0128.76 c ± 0.772.155 ± 0.0643.17 ± 1.891.64 d ± 0.0933.05 c ± 2.671.37 c ± 0.0630.44 c ± 2.192.32 ± 0.1248.27 ± 3.76
Moringa oleifera1.36 d ± 0.0329.25 c ± 2.151.25 e ± 0.0427.28 c ± 1.781.98 ± 0.0542.89 ± 3.041.45 e ± 0.0431.05 c ± 2.061.45 b ± 0.0128.71 c ± 1.812.17 ± 0.04545.40 ± 2.08
Vachellia nilotica2.11 a ± 0.0143.08 a ± 1.161.86 a ± 0.0438.95 a ± 1.483.04 ± 0.0362.55 ± 1.92.30 a ± 0.0247.07 a ± 1.751.95 a ± 0.0440.78 a ± 1.423.27 ± 0.0467.46 ± 2.46
Means with similar letters are not statiscally different at p 0.05.
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Zubair, M.; Yasin, G.; Qazlbash, S.K.; Ul Haq, A.; Jamil, A.; Yaseen, M.; Rahman, S.U.; Guo, W. Carbon Sequestration by Native Tree Species around the Industrial Areas of Southern Punjab, Pakistan. Land 2022, 11, 1577.

AMA Style

Zubair M, Yasin G, Qazlbash SK, Ul Haq A, Jamil A, Yaseen M, Rahman SU, Guo W. Carbon Sequestration by Native Tree Species around the Industrial Areas of Southern Punjab, Pakistan. Land. 2022; 11(9):1577.

Chicago/Turabian Style

Zubair, Muhammad, Ghulam Yasin, Sehrish Khan Qazlbash, Ahsan Ul Haq, Akash Jamil, Muhammad Yaseen, Shafeeq Ur Rahman, and Wei Guo. 2022. "Carbon Sequestration by Native Tree Species around the Industrial Areas of Southern Punjab, Pakistan" Land 11, no. 9: 1577.

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