Air Pollution and Its Mitigation

Abstract

Air is vital for sustaining life on Earth. It is composed of several gases with a definite proportion. The proportionality is disrupted due to intense industrial activities, change of land-use pattern, deforestation, unplanned urbanization, etc. that lead to air pollution. Polluted air has several negative impacts on the survival and health of human beings. All these issues have been discussed in the chapter along with the carbon storage potential of producer community that performs the ecosystem service perfectly by balancing the carbon dioxide budget in the atmosphere.

Annexure 7A: Carbon Storage Potential of Urban Trees

The carbon sequestration of producer community is a function of biomass production capacity, which in turn depends upon interaction between edaphic, climate and topographic factors of an area. Hence, results obtained at one place may not be applicable to another. Therefore, region-based potential of different land types needs to be worked out.

The present research, carried out during 2016, has immense scope as this programme can serve as a road map to minimize the carbon dioxide level and enhance beautification (landscaping) in a cost-effective method.

The objectives of the present research programme are highlighted below:

• Identifying the tree species (in the city of Kolkata and its surrounding areas) having potential for carbon storage

• Selecting tree species with highest efficiency of carbon storage

• Development of allometric equation relating diameter at breast height (DBH) and above ground biomass (AGB) and carbon storage

• Studying the spatial and temporal variations of carbon storage by the selected tree species in the study area.

After a rigorous field work carried out by the present authors, mean values of AGB and AGC data have been presented (Table 7.A.1) for three stations, namely, Kestopur (Station A), Barasat (Station B) and Konnagar (Station C).

Table 7.A.1 List of dominant tree species in the selected stations with their respective AGB and AGC

The AGB of Station A is in the order Ficus religiosa (1143.11) > Ficus benghalensis (1095.66) > Cocos nucifera (914.9) > Delonix regia (854.98) > Eucalyptus globulus (853.95) > Acacia auriculiformis (661.45) > Bombax ceiba (630.03) > Psidium guajava (624.92) > Peltophorum pterocarpum (604.14) > Mangifera indica (474.53) > Polyalthia longifolia (341.39) > Azadirachta indica (307.89) > Ziziphus mauritiana (301.74) > Terminalia arjuna (181.24) > Artocarpus heterophyllus (128.67) > Aegle marmelos (102.00).

Similarly for Station A the order of AGC is Ficus religiosa (544.12) > Ficus benghalensis (523.73) > Cocos nucifera (431.83) > Eucalyptus globulus (403.92) > Delonix regia (399.26) > Acacia auriculiformis (310.88) > Bombax ceiba (294.85) > Psidium guajava (294.85) > Peltophorum pterocarpum (281.53) > Mangifera indica (225.40) > Polyalthia longifolia (160.11) > Azadirachta indica (143.02) > Ziziphus mauritiana (143.02) > Tamarindus indica (137.45) > Terminalia arjuna (85.0) > Artocarpus heterophyllus (61.38) > Aegle marmelos (48.14).

For Station B the order of AGB is Ficus benghalensis (2085.33) > Eucalyptus globulus (1765.9) > Ficus religiosa (1678.9) > Delonix regia (1249.78) > Cocos nucifera (998.78) > Acacia auriculiformis (985.78) > Peltophorum pterocarpum (789.90) > Mangifera indica (789.23) > Psidium guajava (787.04) > Bombax ceiba (786.9) > Polyalthia longifolia (785.23) > Ziziphus mauritiana (675.78) > Artocarpus heterophyllus (654.20) > Tamarindus indica (509.33) > Azadirachta indica (487.12) > Terminalia arjuna (245.89) > Aegle marmelos (234.89).

AGC for Station B follows the order Ficus benghalensis (975.93) > Eucalyptus globulus (831.74) > Ficus religiosa (790.76) > Delonix regia (573.65) > Cocos nucifera (475.42) > Acacia auriculiformis (461.35) > Mangifera indica (379.62) Bombax ceiba (379.29) > Peltophorum pterocarpum (368.09) > > Psidium guajava (365.97) > Polyalthia longifolia (361.21) > Ziziphus mauritiana (320.96) > Artocarpus heterophyllus (315.98) > Tamarindus indica (238.37) > Azadirachta indica (230.38) > Terminalia arjuna (113.11) > Aegle marmelos (112.75).

The order of AGB for Station C is Ficus benghalensis (2091.95) > Ficus religiosa (1989.08) > Delonix regia (1987.66) > Acacia auriculiformis (1268.89) > Psidium guajava (1230.78) > Cocos nucifera (1123.19) > Artocarpus heterophyllus (987.90) > Polyalthia longifolia (987.34) > Mangifera indica (985.66) > Ziziphus mauritiana (980.45) > Eucalyptus globulus (978.45) > Bombax ceiba (961.23) > Azadirachta indica (875.10) > Peltophorum pterocarpum (678.67) > Tamarindus indica (678.45) > Aegle marmelos (356.28) > Terminalia arjuna (345.90).

For Station C the order of AGC is Ficus benghalensis (989.49) > Ficus religiosa (954.76) > Delonix regia (940.16) > Acacia auriculiformis (593.84) > Psidium guajava (577.24) > Cocos nucifera (532.39) > Artocarpus heterophyllus (475.18) > Polyalthia longifolia (467.76) > Eucalyptus globulus (464.76) > Mangifera indica (461.29) > Ziziphus mauritiana (456.89) > Bombax ceiba (450.82) > Azadirachta indica (415.67) > Peltophorum pterocarpum (318.97) > Tamarindus indica (311.41) > Aegle marmelos (169.95) > Terminalia arjuna (164.30) (Figs. 7.A.1, 7.A.2, 7.A.3, 7.A.4, 7.A.5, 7.A.6, 7.A.7, 7.A.8, 7.A.9, 7.A.10, 7.A.11, 7.A.12, 7.A.13, 7.A.14, 7.A.15, 7.A.16 and 7.A.17).

Fig. 7.A.1

AGB and AGC of Cocos nucifera (coconut) from three selected stations

Fig. 7.A.2

AGB and AGC of Azadirachta indica (neem) from three selected stations

Fig. 7.A.3

AGB and AGC of Mangifera indica (mango) from three selected stations

Fig. 7.A.4

AGB and AGC of Tamarindus indica (tamarind) from three selected stations

Fig. 7.A.5

AGB and AGC of Bombax ceiba (shimul) from three selected stations

Fig. 7.A.6

AGB and AGC of Aegle marmelos (bel) from three selected stations

Fig. 7.A.7

AGB and AGC of Terminalia arjuna (arjun) from three selected stations

Fig. 7.A.8

AGB and AGC of Delonix regia (krishnachura) from three selected stations

Fig. 7.A.9

AGB and AGC of Artocarpus heterophyllus (jackfruit) from three selected stations

Fig. 7.A.10

AGB and AGC of Psidium guajava (guava) from three selected stations

Fig. 7.A.11

AGB and AGC of Acacia auriculiformis (akashmoni) from three selected stations

Fig. 7.A.12

AGB and AGC of Ziziphus mauritiana (kul) from three selected stations

Fig. 7.A.13

AGB and AGC of Eucalyptus globulus (eucalyptus) from three selected stations

Fig. 7.A.14

AGB and AGC of Peltophorum pterocarpus (radhachura) from three selected stations

Fig. 7.A.15

AGB and AGC of Polyalthia longifolia (debdaru) from three selected stations

Fig. 7.A.16

AGB and AGC of Ficus religiosa (peepul) from three selected stations

Fig. 7.A.17

AGB and AGC of Ficus benghalensis (banyan) from three selected stations

A simple linear regression equation is the simplest type of representation of the linear models. It is based on the inter-relationship between the response variable (Y) and independent variable (X). It assumes that the relationship is linear in nature and hence can be represented as:

$$ \mathrm{Y}=\mathrm{a}+\mathrm{bX}+\varepsilon $$

where a is the y-intercept of the line and b its slope, and that the residuals are of constant variance, Var (ε) = σ².

When fitting simple linear regression, several outputs need to be analysed. The determination coefficient, more commonly called R², measures the quality of the fit; R² is directly related to the residual variance since:

$$ {\mathrm{R}}^2=1-\frac{{\hat{\sigma}}^2\left(n-2\right)/n}{S_Y^2} $$

where ${S}_Y^2=\left[\sum \limits_n^{ni=1}{\left({Y}_i-\hat{Y}\right)}^2\right]/\mathrm{n}$ is the empirical variance of Y. The difference ${S}_Y^2=\hat{\sigma}\left(\mathrm{n}-2\right)/\mathrm{n}$ between the variance of Y and the residual variance corresponds to the variance explained by the model. The determination coefficient R² can be interpreted as being the ratio between the variance explained by the model and the total variance. It is between 0 and 1, and the closer it is to 1, the better the quality of the fit.

The linear regression equations in all the cases show that DBH (X) can predict the AGB (Y) in a well-fitted manner in all the selected trees as the R² values range between 0.7 and 0.9 in almost all the cases (Table 7.A.2). The scatter plots of all the selected trees are shown in Figs. 7.A.18, 7.A.19, 7.A.20, 7.A.21, 7.A.22, 7.A.23, 7.A.24, 7.A.25, 7.A.26, 7.A.27, 7.A.28, 7.A.29, 7.A.30, 7.A.31, 7.A.32, 7.A.33, 7.A.34, 7.A.35, 7.A.36, 7.A.37, 7.A.38, 7.A.39, 7.A.40, 7.A.41, 7.A.42, 7.A.43, 7.A.44, 7.A.45, 7.A.46, 7.A.47, 7.A.48, 7.A.49, 7.A.50, 7.A.51, 7.A.52, 7.A.53, 7.A.54, 7.A.55, 7.A.56, 7.A.57, 7.A.58, 7.A.59, 7.A.60, 7.A.61, 7.A.62, 7.A.63, 7.A.64, 7.A.65, 7.A.66, 7.A.67 and 7.A.68.

Table 7.A.2 Regression equations with R² values representing the inter-relationship between DBH (X) and AGB (Y)

Fig. 7.A.18

Scatter plot showing the inter-relationship between DBH and AGB of Cocos nucifera (coconut) at Kestopur

Fig. 7.A.19

Scatter plot showing the inter-relationship between DBH and AGB of Cocos nucifera (coconut) at Barasat

Fig. 7.A.20

Scatter plot showing the inter-relationship between DBH and AGB of Cocos nucifera (coconut) at Konnagar

Fig. 7.A.21

Scatter plot showing the inter-relationship between DBH and AGB of Azadirachta indica (neem) at Kestopur

Fig. 7.A.22

Scatter plot showing the inter-relationship between DBH and AGB of Azadirachta indica (neem) at Barasat

Fig. 7.A.23

Scatter plot showing the inter-relationship between DBH and AGB of Azadirachta indica (neem) at Konnagar

Fig. 7.A.24

Scatter plot showing the inter-relationship between DBH and AGB of Mangifera indica (mango) at Kestopur

Fig. 7.A.25

Scatter plot showing the inter-relationship between DBH and AGB of Mangifera indica (mango) at Barasat

Fig. 7.A.26

Scatter plot showing the inter-relationship between DBH and AGB of Mangifera indica (mango) at Konnagar

Fig. 7.A.27

Scatter plot showing the inter-relationship between DBH and AGB of Tamarindus indica (tentul) at Kestopur

Fig. 7.A.28

Scatter plot showing the inter-relationship between DBH and AGB of Tamarindus indica (tentul) at Barasat

Fig. 7.A.29

Scatter plot showing the inter-relationship between DBH and AGB of Tamarindus indica (tentul) at Konnagar

Fig. 7.A.30

Scatter plot showing the inter-relationship between DBH and AGB of Bombax ceiba (shimul) at Kestopur

Fig. 7.A.31

Scatter plot showing the inter-relationship between DBH and AGB of Bombax ceiba (shimul) at Barasat

Fig. 7.A.32

Scatter plot showing the inter-relationship between DBH and AGB of Bombax ceiba (shimul) at Konnagar

Fig. 7.A.33

Scatter plot showing the inter-relationship between DBH and AGB of Aegle marmelos (bel) at Kestopur

Fig. 7.A.34

Scatter plot showing the inter-relationship between DBH and AGB of Aegle marmelos (bel) at Barasat

Fig. 7.A.35

Scatter plot showing the inter-relationship between DBH and AGB of Aegle marmelos (bel) at Konnagar

Fig. 7.A.36

Scatter plot showing the inter-relationship between DBH and AGB of Terminalia arjuna (arjun) at Kestopur

Fig. 7.A.37

Scatter plot showing the inter-relationship between DBH and AGB of Terminalia arjuna (arjun) at Barasat

Fig. 7.A.38

Scatter plot showing the inter-relationship between DBH and AGB of Terminalia arjuna (arjun) at Konnagar

Fig. 7.A.39

Scatter plot showing the inter-relationship between DBH and AGB of Delonix regia (krishnachura) at Kestopur

Fig. 7.A.40

Scatter plot showing the inter-relationship between DBH and AGB of Delonix regia (krishnachura) at Barasat

Fig. 7.A.41

Scatter plot showing the inter-relationship between DBH and AGB of Delonix regia (krishnachura) at Konnagar

Fig. 7.A.42

Scatter plot showing the inter-relationship between DBH and AGB of Artocarpus heterophyllus (jackfruit) at Kestopur

Fig. 7.A.43

Scatter plot showing the inter-relationship between DBH and AGB of Artocarpus heterophyllus (jackfruit) at Barasat

Fig. 7.A.44

Scatter plot showing the inter-relationship between DBH and AGB of Artocarpus heterophyllus (jackfruit) at Konnagar

Fig. 7.A.45

Scatter plot showing the inter-relationship between DBH and AGB of Psidium guajava (guava) at Kestopur

Fig. 7.A.46

Scatter plot showing the inter-relationship between DBH and AGB of Psidium guajava (guava) at Barasat

Fig. 7.A.47

Scatter plot showing the inter-relationship between DBH and AGB of Psidium guajava (guava) at Konnagar

Fig. 7.A.48

Scatter plot showing the inter-relationship between DBH and AGB of Acacia auriculiformis (akashmoni) at Kestopur

Fig. 7.A.49

Scatter plot showing the inter-relationship between DBH and AGB of Acacia auriculiformis (akashmoni) at Barasat

Fig. 7.A.50

Scatter plot showing the inter-relationship between DBH and AGB of Acacia auriculiformis (akashmoni) at Konnagar

Fig. 7.A.51

Scatter plot showing the inter-relationship between DBH and AGB of Ziziphus mauritiana (kul) at Kestopur

Fig. 7.A.52

Scatter plot showing the inter-relationship between DBH and AGB of Ziziphus mauritiana (kul) at Barasat

Fig. 7.A.53

Scatter plot showing the inter-relationship between DBH and AGB of Ziziphus mauritiana (kul) at Konnagar

Fig. 7.A.54

Scatter plot showing the inter-relationship between DBH and AGB of Eucalyptus globulus (eucalyptus) at Kestopur

Fig. 7.A.55

Scatter plot showing the inter-relationship between DBH and AGB of Eucalyptus globulus (eucalyptus) at Barasat

Fig. 7.A.56

Scatter plot showing the inter-relationship between DBH and AGB of Eucalyptus globulus (eucalyptus) at Konnagar

Fig. 7.A.57

Scatter plot showing the inter-relationship between DBH and AGB of Peltophorum pterocarpum (radhachura) at Kestopur

Fig. 7.A.58

Scatter plot showing the inter-relationship between DBH and AGB of Peltophorum pterocarpum (radhachura) at Barasat

Fig. 7.A.59

Scatter plot showing the inter-relationship between DBH and AGB of Peltophorum pterocarpum (radhachura) at Konnagar

Fig. 7.A.60

Scatter plot showing the inter-relationship between DBH and AGB of Polyalthia longifolia (debdaru) at Kestopur

Fig. 7.A.61

Scatter plot showing the inter-relationship between DBH and AGB of Polyalthia longifolia (debdaru) at Barasat

Fig. 7.A.62

Scatter plot showing the inter-relationship between DBH and AGB of Polyalthia longifolia (debdaru) at Konnagar

Fig. 7.A.63

Scatter plot showing the inter-relationship between DBH and AGB of Ficus religiosa (peepul) at Kestopur

Fig. 7.A.64

Scatter plot showing the inter-relationship between DBH and AGB of Ficus religiosa (peepul) at Barasat

Fig. 7.A.65

Scatter plot showing the inter-relationship between DBH and AGB of Ficus religiosa (peepul) at Konnagar

Fig. 7.A.66

Scatter plot showing the inter-relationship between DBH and AGB of Ficus benghalensis (banyan) at Kestopur

Fig. 7.A.67

Scatter plot showing the inter-relationship between DBH and AGB of Ficus benghalensis (banyan) at Barasat

Fig. 7.A.68

Scatter plot showing the inter-relationship between DBH and AGB of Ficus benghalensis (banyan) at Konnagar

Table 7.A.3 presents significant spatial and species-level variations in AGB and AGC.

Table 7.A.3 ANOVA results showing spatial variation of AGB and AGC between selected tree species station wise

Urban, semiurban and rural trees and forests influence climate change but are often disregarded because their ecosystem services are not well-understood or quantified. Trees act as a sink for carbon dioxide by fixing carbon during photosynthesis and storing carbon as biomass. The net long-term carbon dioxide source/sink dynamics of forests change through time as trees grow, die and decay. Trees in urban and semiurban areas currently store carbon which can be emitted back to the atmosphere after tree death and sequester carbon as they grow. Urban and semiurban trees also influence air temperatures and building energy use and consequently alter carbon emissions from numerous urban sources (e.g. Power Plants) (Nowak 1993). Thus urban trees influence local climate, carbon cycles, energy use and climate change (e.g. Abdollahi et al. 2000; Wilby and Perry 2006; Gill et al. 2007; Nowak 2010; Lal and Augustine 2012).

A study conducted in the United States reveals that urban areas in the conterminous United States have increased from 2.5% of the US land area (19.5 million ha) in 1990 to 3.1% (24.0 million ha) in 2000, an increase in area the size of Vermont and New Hampshire combined. If the growth patterns of 1990s continue, urban land is projected to reach 8.1% by 2050, an increase greater than the area of Montana. Within these urban areas, tree cover is estimated at 35% (Nowak and Greenfield 2012). Given the growing expanse of urban areas, trees within these areas have the potential to store and annually sequester substantial amounts of carbon. Understanding this national carbon affect can aid in preparing annual inventories of greenhouse gas (GHG) emissions and sinks (US EPA 2010; Heath et al. 2011). Numerous cities in the United States have analysed carbon storage and sequestration of the trees and forests among various land-use types using the standard methodology (www.itreetools.org) (Hutyra et al. 2011; Chaparro and Terradas 2009; Zhao et al. 2010; Davies et al. 2011; Strohbach and Haase 2012).

In the past city analyses of carbon storage and sequestration have been extrapolated to national estimates using limited data. The first estimate of national carbon storage by urban trees (between 350 and 750 million tonnes, Nowak 1993) was based on an extrapolation of Carbon Data from one city (Oakland, C.A.) and tree cover data from various US cities (e.g. Nowak et al. 1996). A later assessment which included data from a second city (Chicago, IL) estimated National Carbon Storage by urban trees between 600 and 900 million tonnes (Nowak 1994). The most recent analysis which uses data from ten cities and urban tree cover estimates (Nowak et al. 2001) derived from 1991 Advanced Very High Resolution Radiometer (AVHRR) data, estimated National Carbon Storage by urban forests at 700 million tonnes (range 335 million to 980 million tonnes) (Nowak and Crane 2002). Above and below ground biomass in all forestland across the United States, which includes forest stands within urban areas, stored approximately 20.2 billion tonnes of carbon in 2008 (Heath et al. 2011).

These carbon storage and sequestration estimates provide better, more up-to-date information for National Carbon estimates (e.g. IPCC 2006) and can be used to help assess the actual and potential role of urban forests in reducing atmospheric carbon dioxide. Although several studies have been carried out on carbon sequestration by carbon trees in several cities of the world, very few literatures are available on the potential of trees of Kolkata on carbon sequestration.

There are, however, some studies on Bangalore (Sudha and Ravindranath 2000; Nagendra and Gopal 2010), Visakhapatnam City (Mitra 1993; Madan 1993), Chandigarh (Chaudhary 2006; Chaudhary and Tewari 2010; FSI 2009) and Delhi (FSI 2009). Similar studies such as biodiversity and carbon storage are also available for Bhopal (Dwivedi et al. 2009), Delhi (Khera et al. 2009), Jaipur (Verma 1985; Dubey and Pandey 1993), Mumbai (Zérah 2007) and Pune (Patwardhan et al. 2001). A few studies are also available for specific locations within the urban ecosystems, such as NEERI Campus, Nagpur (Gupta et al. 2008), Indian Institute of Science Campus, Bangalore (Mhatre 2008) and Bangalore University Campus at Jnanabharathi (Nandini et al. 2009). Many policy and robust scientific evidences in last two decades have emphasized the critical necessity of green areas within urban social-ecological systems to ameliorate several problems of city-living; however the trend of urban ecology and application of its principles are still lagging behind.

Roadside trees, because of their proximity to the generation of vehicle emissions, are important in reducing pollution. Beckett et al. (2000) found that roadside trees capture more large-size particulate matter than trees not near the road. These effects have implications for air quality standards. Roadside trees additionally have aesthetic value to residents and high ecological value to urban areas as part of our green infrastructure. Urban trees perform important ecological function in cities by sequestering carbon and reducing automobile pollution. The net save in carbon emissions that can be achieved by tree planting can be up to 18 kg CO₂/year per tree, and this benefit corresponds to that provided by 3–5 forest trees of similar size and health (Francesco 2011).

The amount of carbon stored in a tree depends upon its biomass and growth pattern. It is found that fast-growing trees seize more carbon than slow-growing trees (Montagnini and Porras 1998; Redondo-Brenes 2007). In the long term, the amount of carbon accumulated by slow-growing species is larger than by fast-growing species. This indicates faster-growing trees may accumulate larger amount carbon in early stage of their life, while high specific gravity of slow growing trees allow them to accumulate more carbon in longer.

In the present study, Ficus benghalensis shows the highest AGB and AGC for all the stations, and Aegle marmelos shows the lowest AGB and AGC for Stations A and B. For Station C the lowest AGB and AGC is found in Terminalia arjuna.

ANOVA results show significant spatial variations of AGB and AGC between the selected stations as well as between the selected tree species due to variation in edaphic factors.

The significant AGB and AGC variations between species and stations indicate that carbon storage potential is regulated not only by edaphic factors (that are different for different locations or stations) but also on the type of the species. Species with C₃ and C₄ modes of photosynthesis have different response to climatic conditions. From the entire result, it can be concluded that urban and semiurban trees are unique store house of carbon. As the diameter of floral species increases, its biomass and carbon storage capacity also increases resulting in the sequestering of more carbon, which removes more carbon dioxide from the atmosphere. Species should be planted considering all environmental parameters (location/biogeographic zone, climate, soil type, annual temperature, groundwater availability, annual rainfall, etc.) in mind. Also the species which harvest more CO₂ from the atmosphere should be planted more.

The author personally feels that plantation of terrestrial plants other than mangroves in the intertidal mudflats of Sundarbans can reduce considerable carbon dioxide level in the atmosphere at local scale. The trees discussed here are abundantly available on the roadside and villages in and around the city of Kolkata. With their considerable biomass, they can serve as potential sink of carbon and reduce the atmospheric carbon dioxide at local scale.