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Environmental Science and Pollution Research
https://doi.org/10.1007/s11356-022-19505-3
RESEARCH ARTICLE
Assessing theenvironmental impact ofair pollution oncrops
bymonitoring air pollution tolerance index (APTI) andanticipated
performance index (API)
LalChandMalav1,2· SandeepKumar2· SadikulIslam3· PriyaChaudhary2· ShakeelA.Khan2
Received: 3 December 2021 / Accepted: 24 February 2022
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
Abstract
Air pollutants adversely affect the physiological, biochemical parameters, and productivity of the crops, but scarce and
meager reports are available to know the certain impact of air pollution on crops. The aim of the present study was to assess
environmental impact of air pollutants on biochemical parameters of the crops by monitoring two important indicators, i.e.,
Air Pollution Tolerance Index (APTI) and Anticipated Performance Index (API). These two indicators provide the sensitivity
and the tolerance level of the crops towards the air pollutants. Seven different crops were selected in four different locations
in the vicinity of a thermal power plant. The results depicted the maximum aerial particulate matter deposition on crop
canopy (ADCC) in barley (Hordeum vulgare 2.15mg/cm2) and wheat (Triticum aestivum 2.21mg/cm2). The maximum APTI
value was found in berseem (Trifolium alexandrinum, 9.45 and 11.44) during the first and second year of study, respectively.
Results indicated that all crops were sensitive to air pollution in the selected area, but berseem (Trifolium alexandrinum)
was less sensitive in comparison to other crops. API value showed that wheat (Triticum aestivum) and rice (Oryza sativa)
were best-suited crops in the selected study area as compared to other crops. It has been found in the study that the API and
APTI are two important indicators for the selection of crops in the severe air polluting area.
Keywords Air pollutant exposure· Particulate matter· Air pollution tolerance index (APTI)· Anticipated performance
index (API)· Crops· Aerial deposition
Introduction
In the current scenario, rapid industrialization, urbanization,
and population trudge in an unsustainable manner are the
noteworthy reasons behind the deterioration of the envi-
ronment (Chaudhary etal. 2021). Air pollution is a type
of these maladies, which affects the normal functioning of
living and non-living beings (Bui etal. 2021; Kumar etal.
2019; Hariram etal. 2018). Motor vehicles, small and large
scale industries, construction sites, landfills, and burning of
agriculture residue are so many anthropogenic sources of
air pollution (Mandal and Sengupta 2003). But coal-based
power generation sector contributes more to air pollution
along with the transportation sector. Globally, we see that
coal-based power plant, followed by gas, is the largest source
of electricity production. In recent decades, the power gen-
eration sector has grown manifold in our country to fulfill
the electricity requirements; hence, the consumption of
coal has increased. At present, coal is the principal energy
source in India, which is used for near about 59.1% of elec-
tric power generation in the country (CEA 2021). In general,
low-grade quality coal is used in the Indian thermal power
plants (TPPs), and combustion of this type of coal in TPPs
contributes to high emission of particulate matter, primarily
fly ash (Hariram etal. 2018). Studies from different coun-
tries have shown a significant environmental impact of coal-
fired power plants on its surroundings (Alastuey etal. 1999;
Sharma etal. 2005; Xie etal. 2006). The primary emissions
from coal combustion at TPPs are carbon dioxide, nitrogen
Responsible Editor: Philippe Garrigues
* Shakeel A. Khan
shakeel_iari@yahoo.com; shakeel.khan@icar.gov.in
1 ICAR-National Bureau ofSoil Survey & Land Use Planning,
RC, Udaipur313001, India
2 Division ofEnvironmental Science, ICAR-Indian
Agricultural Research Institute, NewDelhi110012, India
3 ICAR-Indian Institute ofSoil andWater Conservation,
Dehradun, India
Environmental Science and Pollution Research
1 3
oxides, sulfur oxides, and airborne inorganic particles such
as fly ash, soot, and other trace gases (Malav etal. 2017).
In the last three decades, however, changes in the pattern
of air pollutant emissions, including increases in those from
TPPs and motor vehicles have led to more significant pollut-
ant impacts in more remote rural areas. In developing coun-
tries, most of the emphasis was given on human health in
metro cities. However, air pollution in urban and peri-urban
areas could also have significant impacts on agricultural
production. If any alteration occurs in our environment, it
directly or indirectly affects the physiological and biological
growth of the plants (Panda etal. 2018; Hamraz etal. 2014).
Generally, particulate matter is deposited on the leaf surface
of the crops as per the specific morphological feature of the
leaves (Seyyednejad etal. 2011), and this particulate matter
deposition causes the several adverse impacts on the physiol-
ogy of the crop plants. Air pollutants have harmful impacts
on the crop plant’s health to their reactive nature (Prusty
etal. 2005; Das and Prasad 2010; Gupta etal. 2015). A
considerable amount of damage caused to vegetation by the
air pollutants showing the bodily harm of leaves as a result
of dust deposition, inhibition of photosynthesis, and protein
synthesis (Saha and Padhy 2011).
Deposition of particulate matter on the leaf surface
majorly affects the level of ascorbic acid, chlorophyll, and
carotenoids. It also affects the pH of the cell sap and relative
water content (RWC) (Chaudhary etal. 2021). Some stud-
ies have been conducted for different types of air pollution
sources to assess the impact of particulate matter deposi-
tion and its impact on biochemical parameters of crop plants
(Naidoo and Chirkoot 2004; Nanos and Ilias 2007; Prajapati
and Tripathi 2008; Rai and Panda 2014; Gupta etal. 2015,
2016). Pathak etal. (2011) reported that, the production of
reactive oxygen species (ROSs) is the foremost cause behind
the reduction in chlorophyll content of the leaves under the
polluted situations. ROSs is tiny reactive molecule that can
damage cell structures during stress conditions. Scaveng-
ing of ROSs might be an effective strategy to control these
molecules. Ascorbic acid in the plant leaves plays the role
of scavenger to protect the cell membranes from oxidative
damage caused by ROSs under such polluted conditions
(Tambussi etal. 2000; Smirnoff 1996). Hence, ascorbic
acid plays a significant role in the defense mechanism of
the crops. An increased level of ascorbic acid in the leaves
indicates higher pollution tolerance of the plants. Rai and
Panda (2014) reported that low level of ascorbic acid might
be attributed to its role in the removal of ROSs or free radi-
cals generated due to reactions of pollutants in the leaves.
Generally, plants have different responses like sensitive,
tolerant, and hardy towards the air pollution. Life cycle and
interactions of plants with the environment also depends on
this nature. Therefore, with the categorization of the plants,
whether sensitive or tolerant towards the air pollution, the
air pollution tolerance index (APTI) was developed. APTI is
calculated using four biochemical parameters like ascorbic
acid content, chlorophyll content, leaf extract pH, and rela-
tive water content of leaves of plants (Liu and Ding 2008;
Bharti etal. 2018). Plants are affected by a lot of stress dur-
ing its life cycle. So with the help of a single character, we
cannot justify whether a plant is sensitive or tolerant to that
particular stress like air pollution. Therefore, four biochemi-
cal parameters (ascorbic acid content, chlorophyll content,
leaf extract pH, and relative water content) are necessary
to build up APTI (Raza etal. l985; Dwivedi and Tripathi
2007). Air pollution affects not only growth, physiological,
and biochemical parameters of the crop plant, but also it
has some drastic impacts on biological and socioeconomic
characteristics. That is why to assess an overall reduction
in the effects of air pollutants on crops, the anticipated per-
formance index (API) was developed. APTI includes only
biochemical parameters, whereas API includes socioeco-
nomic and biological parameters (Govindaraju etal. 2012;
Ogunkunle etal. 2015; Zhang etal. 2016; Achakzai etal.
2017). Crops with higher API value are tolerant to air pol-
lution, whereas a lower value that depicts crops is sensitive
(Sahu etal. 2020). Hence, tolerant crops could be suggested
to the farmers to get better yields and production than sen-
sitive crops in polluted areas. Sensitive crops can be used
as indicators of pollution (Krishnaveni etal. 2013). During
research on APTI, researchers have mainly focused only on
trees species, but APTI of crops has not much discussed yet.
Therefore, it was necessary to focus on the crops in rela-
tion to air pollution because crops are the central pillar to
maintain food security and sustainability of the ecosystem.
In the present study, the susceptible levels of different
crops grown in the vicinity of coal-based TPP of National
Thermal Power Corporation (NTPC), Dadri were appraised
based on their APTI and API values. To assess the effects
of air pollutants from coal-based TPP on biochemical prop-
erties of crop species, the following analyses were carried
out: (1) Assessment of air pollution load at selected sites
in the vicinity of TPP; (2) Assessment of the impact of air
pollution load on biochemical parameters of different crop
species, and (3) estimation of tolerant crop species growing
at selected sites surrounding to NTPC, Dadri based on their
APTI and API values.
Materials andmethods
Study area
The study was conducted in the vicinity of NTPC, Dadri.
It is located in the Gautam Budh Nagar district of Uttar
Pradesh (28°35′54″N &77°36′34″E) (Fig.1). The experi-
ment was conducted during Kharif (June to October) and
Environmental Science and Pollution Research
1 3
Rabi (November to March) seasons of 2016–2017 and
2017–2018. During 2016–2017, the annual mean maxi-
mum and minimum temperature were 36.5°C and 24.4°C,
respectively, with 30.5°C annual average temperature. The
annual mean maximum and minimum temperature during
2017–2018 were 35.8°C and 23.3°C with 29.6°C annual
average temperature. The average annual rainfall of the
district is 786mm. The maximum rainfall occurs during
the monsoon months from the end of June to the middle of
September. The wind speed varied from 8.7 to 13km h−1
during the summer and 9.3 to 11.9km h−1 during the winter.
The wind direction was predominantly north-westerly during
the whole growing period (as shown in windrose diagram,
Fig.2).
Selection ofsampling Site
Around the TPP, four sites were selected according to the
dominant wind direction and distance from the power plant.
Out of four, one site was identified towards the windward
direction to the TPP (Akilpur Jagir). Dominant wind direc-
tion throughout the year was in the north-west (NW). One
site was selected in a leeward direction (Khangoda), and two
were chosen perpendicular to the dominant wind direction
(Nidhauli and Ranauli Latifpur) (Table1).
Crop selection andsample collection
Seven crops such as Oryza sativa (Variety- Pusa 1509),
Triticum aestivum (Variety- HD 2967), Pennisetum glau-
cum (pearl millet), Sorghum bicolor (Sorghum), Hordeum
vulgare L. (Barley), Brassica juncea (Mustard), and Trifo-
lium alexandrinum L. (Berseem) were taken in all selected
sites in the study area during kharif and Rabi seasons in
2016–2017 and 2017–2018. Farmers field was selected
which grown same crop variety and following almost simi-
lar crop management practices. Fully fresh mature leaves (at
maturity stage) of each crop plants were randomly collected
Fig. 1 Study area: NTPC, Dadri, Gautam Budh Nagar, UP, India
Environmental Science and Pollution Research
1 3
in triplicate during the morning hours (08:00–11:30 AM).
After that, leaf samples were immediately brought to the
laboratory in polythene bags in the ice box for further bio-
chemical and dust deposition analysis.
Experimental calculations
Monitoring ofambient level ofair pollutants
The monitoring of the primary and secondary pollutants,
i.e., NO2 (Nitrogen dioxide), SO2 (Sulfur dioxide) and SPM
(Suspended Particulate matter), were carried out as per the
guidelines that are given by the Central Pollution Control
Board (CPCB). Particulate matter, SO2 and NO2, were ana-
lyzed by the Gravimetric method, Improved West and Gaeke
method (1956) (CPCB 2012), and Jacob & Hochheiser mod-
ified method (1958) (CPCB 2012), respectively. Monitoring
was done at monthly interval basis at each selected site. Air
monitoring for SO2, NO2, and SPM was done for 24h per
month at each of the sites during the growing season while
for ozone, 8-h sampling was done as per the CPCB guide-
lines (2011). Furthermore, air quality index (AQI) was cal-
culated by using the formula given by (Rao and Rao 1998).
Where (SO2), (NOx), and (SPM) represent the individual
concentration and Sso2, SNOX, SSPM represents the ambient
air quality standards for SO2, NOx, and Suspended Particu-
late Matter (SPM), respectively. National ambient air quality
standards (CPCB 2009) were given by CPCB (CPCB 2012).
The rating scale for AQI value was given by Rai and Panda
(2014).
Aerial depositions oncrop canopy (ADCC)
Deposition of particulate matter on crop canopy was also
measured as per the methodology of Manisha etal. (2016);
Prusty etal. (2005). Leaf samples were randomly collected
in a beaker and washed thoroughly by a hairbrush with dis-
tilled water. The water in the beaker was evaporated entirely
in an oven at 100°C and weighed.
The following equation quantified dust load:
Where, W is the amount of dust load (mg/cm2);
w1
is the
initial weight of beaker without dust;
w2
is the final weight
of the beaker with dust; A is the total area of the leaf (cm2).
Analysis ofbiochemical parameters
The mature fresh leaf samples were analyzed for total
chlorophyll and ascorbic acid by the method given by
Arnon (1949) and Sadashivam and Manikam (1991),
respectively. Fresh leaf (0.5g) sample was homogenized
using 50ml deionized water and the supernatant obtained
after centrifugation was collected for detection of pH using
the digital pH meter. The percentage relative water content
AQI =
1
∕
3
(
SO
2∕
Sso
2+
NO
X∕
S
NOx +
SPM
∕
S
SPM )×100
W=(
w
2−
w
1)∕
A
.
Fig. 2 Windrose map NTPC,
Dadri
0
2
4
6
8
10
12
14
16
N
NNE
NE
ENE
E
ESE
SE
SSE
S
SSW
SW
WSW
W
WNW
NW
NNW Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Table 1 Selected sites along with latitude and longitude
Village Direction
to NTPC
Aerial distance
from TPP (km)
Latitude Longitude
Akilpur Jagir NW 4.5 28.615427 77.562230
Khangoda SE 3.5 28.590027 77.627510
Ranauli Lat-
ifpur
SW 4.0 28.579108 77.583039
Nidhauli NE 3.0 28.618532 77.627541
Environmental Science and Pollution Research
1 3
was calculated by using the initial weight, turgid weight,
and dry weights of leaf samples (Liu and Ding 2008).
Calculation ofair pollution tolerance index (APTI)
The APTI was calculated by the given formula (Singh and
Rao (1983)) as follows:
where:
A: Ascorbic acid (in mg/g)
T: Total chlorophyll (in mg/g)
P: pH of the leaf extract
R: Relative water content of leaf (per cent)
Calculation ofanticipated performance index (API)
After the computation of APTI for all the crop species,
we calculated API based on final APTI values with some
socioeconomic and biological characters and different
grades like (+ , −) were given to the all crop species based
on these parameters. The API value of a particular crop
is based on the percentage score obtained. The percent-
age score can be calculated as given formula (Govindaraju
etal. 2012; Ogunkunle etal. 2015; Achakzai etal. 2017).
% score
=Gradesobt ainedbycropspecies
Maximumpossiblegradesf oranycropspecies
∗
100
Statistical analysis
Descriptive statistical analysis was conducted with all the
data set of 2016–2017 and 2017–2018 during Kharif and
Rabi season. The means of air pollutant levels like NO2,
SO2, and SPM concentrations; AQI value and biochemi-
cal parameters; and concentration of crop plant species in
each site were evaluated using Tukey’s Honest significant
difference (THSD) test (p = 0.05). The Pearson correla-
tion matrix was constructed to measure the linear associa-
tion between air pollutants and biochemical parameters
of crops (p = 0.05, 0.01, and 0.001). Linear regression
analysis was conducted between APTI and four biochemi-
cal parameters (pH, Relative water concentration RWC,
Ascorbic acid, Total chlorophyll) of crops separately to
develop predictive models. Principal component analysis
(PCA) and hierarchical cluster conducted for the air pol-
lutants were considered in the study using the Euclidean
distance method to identify different air and clustering
the sites with similar air pollutant concentration. All the
statistical analysis was performed using Microsoft excel
and RStudio (with R version 4.0.2).
APTI
=
1
10
[A(T+P)+R
]
Quality assurance andquality control
Quality assurance and quality control were properly main-
tained during the experiment. The pieces of equipment are
maintained and operated according to the standard specifica-
tions of the supplier. We have adopted US EPA’s standard
operating procedures for instrument calibration and mainte-
nance. We visited the sites regular basis. On a monthly basis,
a review meeting was conducted. The quality of sampling
and analysis was ascertained by analyzing blank. Whereas
in the case of plant sample collection, freshly plucked foliar
samples were firstly kept in sealed polythene and an icebox
and then brought to the laboratory for further investigation.
For analysis of different biochemical parameter, the foliar
samples were taken as triplicate. During biochemical analy-
sis, the margin and midrib portions were avoided in all the
triplicates to ensure the authenticity of the results. The QC
procedures for the air sampling and monitoring sections
include preventative maintenance of equipment, calibration
of equipment, and analysis of field blanks and lab blanks.
Results anddiscussion
General statistical andcharacteristics ofair quality
Air quality monitoring showed that concentration of PM,
SO2, and NO2 was found minimum at Akilpur Jagir (least
polluted site). In contrast, maximum level was found at
Khangoda (most polluted site) due to their location in the
leeward side to the TPP. PM, SO2, and NO2 concentration
were maximum in Rabi and minimum in the Kharif season.
Other villages had a concentration in between the maxi-
mum and minimum, as shown in Table2. Similar types of
results found in the second year also. Agrawal etal. (2003)
also observed that the higher air pollutants concentration
at industrial area and concentration of SPM, SO2, and NO2
were max in the winter while O3 concentration was higher
in the summer. Joshi etal. (2008) also showed similar types
of results during the summer and winter season. Sharma
etal. (2019) investigated air pollution levels in several parts
of Delhi, finding up to 35μg/m3 SO2 and 121μg/m3 NO2.
Ozone concentrations ranged from 10.1 to 86.4μg/m3. Korh-
ale etal. (2021) found NOx concentrations of up to 82.1μg/
m3 and ozone concentrations of up to 96.1g/m3. AQI values
were varied from 68 to 137 and 72 to 126 in Kharif and
Rabi season, respectively, in 2016–2017. Whereas during
2017–2018, AQI ranged from 43 to 124 and 52 to 141 in
Kharif and Rabi season, respectively. AQI values showed
that air pollution level was light to moderate at least polluted
site (Akilpur jagir), whereas heavy to severe pollution level
at other selected sites (Khangoda, Ranauli Latifpur).
Environmental Science and Pollution Research
1 3
Aerial depositions oncrop canopy (ADCC)
Estimation of an aerial deposition on crop canopies showed
maximum deposition was found at Khangoda (Most pol-
luted site) and minimum at Akilpur Jagir (Least polluted
site) and it ranged from 0.56 to 2.15mg/cm2 in different
crop species at selected sites. Dust accumulation capacity
was maximum in Hordeum vulgare (2.15mg/cm2) and the
minimum in Brassica juncea (1.06mg/cm2) at polluted site
during 2016–2017. Whereas dust accumulation capacity was
maximum in Triticum aestivum (2.21mg/cm2) and the mini-
mum in Trifolium alexandrinum (1.06mg/cm2) at polluted
site during 2017–2018 (Table3, Table4). In Rabi crops,
atmospheric deposition load on leaves was found to be more
than Kharif crops. This is primarily due to the less rainfall
during Rabi season than the Kharif season. Higher rainfall
in Kharif might have washed out some of the deposited par-
ticulate matter. Earlier co-workers reported similar types
of results. Chakrabarti etal. (2014) reported that in wheat
crop, atmospheric deposition load on leaves was found to
be more than rice at all stages and deposition was gradually
decreased with increasing distance from TPP. According to
Rai etal. (2013), the dust deposition potential on tree spe-
cies was highest at the polluted site as compared to the least
polluted site and varied species to species in the study area.
Bharti etal. (2018) examined that aerial deposition on plant
Table 2 Seasonal mean NO2, SO2, and SPM concentrations (μg/m3) during summer and winter seasons during 2016–2017 and 2017–2018
Sr. No Sites NO2 (μg/m3) SO2 (μg/m3) SPM (μg/m3)AQI
Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi
2016–2017
1 Akilpur Jagir 18.51 ± 0.96 19.4 ± 1.34 8.98 ± 0.64 10.58 ± 0.93 169 ± 15 180 ± 16 68 72
2 Khangoda 37.35 ± 2.3 39.32 ± 1.99 16.04 ± 2.01 18.58 ± 1.37 343 ± 42 305 ± 47 137 126
3 Ranauli Latifpur 24.5 ± 3.6 27.57 ± 2 12.89 ± 0.3 14.58 ± 1.12 222 ± 14 262 ± 11 90 105
4 Nidhauli 23.56 ± 2.75 27.71 ± 2.71 12.71 ± 1.23 14.37 ± 0.09 218 ± 19 244 ± 15 88 99
2017–2018
1 Akilpur Jagir 21.44 ± 1.66 23.44 ± 2.22 10.84 ± 0.77 12.32 ± 0.83 90 ± 16 110 ± 15 43 52
2 Khangoda 36.09 ± 2.43 42.66 ± 1.98 17.20 ± 1.85 22.07 ± 2.08 305 ± 22 343 ± 20 124 141
3 Ranauli Latifpur 28.87 ± 3.22 30.42 ± 1.87 15.81 ± 0.32 18.58 ± 1.15 222 ± 36 262 ± 38 93 108
4 Nidhauli 27.05 ± 2.47 30.43 ± 2.32 14.62 ± 1.17 17.82 ± 1.55 218 ± 23 250 ± 21 30 103
Table 3 Aerial deposition on crop canopy (mg/cm2) (2016–2017)
Values in the Kharif season crop: column 1 to 3 followed by same letter are not significantly different according to Tukey’s Honest test
(p = 0.05). Similarly, Rabi season crop: columns 4 to 7, with same letter are not significantly different
Sites Oryza sativa Pennisetum
glaucum Sorghum bicolor Triticum aesti-
vum Hordeum vul-
gare Brassica juncea Trifolium alex-
andrinum
Akilpur Jagir 0.88 ± 0.12l 1.05 ± 0.11j0.97 ± 0.16k 1.17 ± 0.12f1.05 ± 0.08i0.56 ± 0.14o0.72 ± 0.13n
Khangoda 2.07 ± 0.16b2.11 ± 0.13a1.93 ± 0.14b1.98 ± 0.09b2.15 ± 0.14a1.06 ± 0.18h 1.11 ± 0.13g
Ranauli Latifpur 1.2 ± 0.11i1.89 ± 0.1d1.67 ± 0.12f1.39 ± 0.12e1.68 ± 0.1c0.77 ± 0.15m 1.02 ± 0.07e
Nidhauli 1.34 ± 0.08h 1.72 ± 0.09e1.52 ± 0.08g 1.44 ± 0.1d1.44 ± 0.1d0.89 ± 0.11k 0.82 ± 0.09l
Table 4 Aerial deposition on crop canopy (mg/cm2) (2017–2018)
Values in the Kharif season crop: column 1 to 3 followed by same letter are not significantly different according to Tukey’s Honest test
(p = 0.05). Similarly, Rabi season crop: columns 4 to 7, with same letter are not significantly different
Sites Oryza sativa Pennisetum
glaucum Sorghum bicolor Triticum aesti-
vum Hordeum vul-
gare Brassica juncea Trifolium alex-
andrinum
Akilpur Jagir 0.69 ± 0.07k 0.98 ± 0.08j1.05 ± 0.14i1.31 ± 0.06h 1.1 ± 0.14i0.68 ± 0.4n0.65 ± 0.1o
Khangoda 1.56 ± 0.09e1.7 ± 0.12c1.98 ± 0.07a2.21 ± 0.1a2.01 ± 0.17b1.34 ± 0.17g 1.06 ± 0.09j
Ranauli Latifpur 1.11 ± 0.08h 1.56 ± 0.09e1.75 ± 0.11b1.66 ± 0.09e1.78 ± 0.11d0.98 ± 0.14k 0.97 ± 0.12l
Nidhauli 1.21 ± 0.06g 1.69 ± 0.15d1.44 ± 0.13f1.87 ± 0.11c1.56 ± 0.12f1.06 ± 0.11j0.82 ± 0.12m
Environmental Science and Pollution Research
1 3
leaves varied from 0.1 to 5.7, and 0.07 to 5.4mg/cm2 at
industrial and control sites, respectively, and these results
were similar to our study. Mina etal. (2018) conducted a
study to evaluate the impact of particulate matter deposi-
tion on biochemical parameters of the rice crop cultivars;
it has been found that the higher deposition may block the
gaseous exchange through stomata, which ultimately affects
biochemical activities of the crop plants.
Effect ofair pollutants onbiochemical parameters
ofthecrops
The collected leaf samples were analyzed for biochemical
parameters like pH, total chlorophyll, ascorbic acid, and
relative water content to calculate APTI (Table5, Table6).
Results showed that leaf extract pH ranged from 5.89 to
7.87, 5.4 to 6.12, 5.8 to 6.68, and 5.79 to 6.48 at Akilpur
Jagir, Khangoda, Ranauli Latifpur, and Nidhauli villages,
respectively, in different crop species and maximum pH
was found in Sorghum bicolor whereas the minimum in
Oryza sativa crop at Khangoda (most polluted site) dur-
ing 2016–2017. During 2017–2018, we found maximum
pH value in Trifolium alexandrinum and the minimum in
the Oryza sativa crop. Range of pH value at different sites
was almost similar to the previous year. When acidic pollut-
ants like SO2 and NO2 are absorbed to the leaf of the crop
plants, it may be converted into acid like H2SO4, HNO3,
which cause a reduction in pH value of the cell sap. Lower
pH value indicates more sensitiveness towards air pollu-
tion, whereas a higher pH value shows more tolerance (Esc-
obedo etal. 2008). Scholz and Reck (1977) also showed
similar types of results. Joshi and Chauhan (2008) reported
a decrease in pH value at polluted sites as compared to less
or no polluted sites. For the biological activity of the organ-
ism, some enzymes are pH-dependent (Karmakar and Padhy
2019). The photosynthetic efficiency is also influenced by
Table 5 Biochemical parameters and APTI values for different crop species at selected sites during 2016–17
Values in the same column (within same sub-table) followed by same letter are not significantly different according to Tukey’s Honest test
(p = 0.05)
Crops Sites RWC (%) pH Total chlorophyll (mg/g) Ascorbic acid (mg/g) APTI
Oryza sativa Akilpur Jagir 87.54 ± 2.62ab 6.57 ± 0.03c2.92 ± 0.06bc 2.24 ± 0.07a10.88c
Khangoda 78.54 ± 3.39cd 5.4 ± 0.03g 2.54 ± 0.06bcd 1.27 ± 0.03f8.86l
Ranauli Latifpur 82.04 ± 4.32bcd 5.94 ± 0.02f2.6 ± 0.07cd 1.42 ± 0.06d9.42h
Nidhauli 82.15 ± 3.27bcd 6.21 ± 0.02de 2.67 ± 0.09bcd 1.40 ± 0.03d9.46g
Pennisetum glaucum Akilpur Jagir 91.3 ± 1.92a7.26 ± 0.06b3.25 ± 0.08a2.11 ± 0.07a11.35a
Khangoda 82.11 ± 4.09bcd 5.89 ± 0.03f2.23 ± 0.04ef 1.41 ± 0.06d9.36i
Ranauli Latifpur 88.51 ± 1.63ab 6.12 ± 0.06e2.56 ± 0.05cde 1.67 ± 0.04b10.30d
Nidhauli 86.7 ± 2.86abc 6.48 ± 0.05c2.66 ± 0.02bcd 1.64 ± 0.05c10.17e
Sorghum bicolor Akilpur Jagir 87.94 ± 2.07ab 7.87 ± 0.05a2.97 ± 0.12ab 2.24 ± 0.03a11.22b
Khangoda 78.54 ± 2.52e6.12 ± 0.04e2.14 ± 0.03f1.30 ± 0.07ef 8.93k
Ranauli Latifpur 75.39 ± 3.16d6.3 ± 0.04d2.43 ± 0.04cde 1.65 ± 0.04b8.98j
Nidhauli 85.85 ± 3.01abc 6.48 ± 0.05c2.39 ± 0.08def 1.37 ± 0.06de 9.80f
Triticum aestivum Akilpur Jagir 91.21 ± 1.69ab 6.11 ± 0.05cd 3.31 ± 0.07b2.34 ± 0.05b11.33c
Khangoda 82.17 ± 3.23cdef 5.77 ± 0.03e2.71 ± 0.06ef 1.28 ± 0.07j9.30m
Ranauli Latifpur 86.82 ± 2.43abcde 5.8 ± 0.02e2.8 ± 0.04e1.44 ± 0.07h 9.92i
Nidhauli 89.74 ± 2.74abcd 5.79 ± 0.05e2.75 ± 0.07ef 1.64 ± 0.04ef 10.37e
Hordeum vulgare Akilpur Jagir 92.11 ± 2.42a6.76 ± 0.05b3.12 ± 0.07c2.54 ± 0.04a11.72b
Khangoda 78.71 ± 3.52ef 5.7 ± 0.04e2.57 ± 0.05gh 1.62 ± 0.04f9.21n
Ranauli Latifpur 82.44 ± 3.8cdef 6.05 ± 0.08d2.67 ± 0.08fg 1.70 ± 0.06d9.73j
Nidhauli 90.19 ± 1.96abc 6.22 ± 0.03c2.8 ± 0.1e1.73 ± 0.03c10.58d
Brassica juncea Akilpur Jagir 83.41 ± 2.89bcdef 6.8 ± 0.05b2.92 ± 0.03d1.74 ± 0.04c10.03h
Khangoda 76.19 ± 3.03f5.52 ± 0.05f2.57 ± 0.05gh 1.37 ± 0.06i8.73o
Ranauli Latifpur 79.82 ± 4.16ef 5.8 ± 0.05e2.6 ± 0.04gh 1.46 ± 0.05h 9.21n
Nidhauli 80.19 ± 4.09ef 5.97 ± 0.07d2.72 ± 0.09ef 1.53 ± 0.03g 9.35l
Trifolium alexandrinum Akilpur Jagir 92.26 ± 1.03a7.54 ± 0.06a3.42 ± 0.04a2.34 ± 0.07b11.79a
Khangoda 81.5 ± 3.52def 6.1 ± 0.05cd 2.37 ± 0.04i1.54 ± 0.05g 9.45k
Ranauli Latifpur 85.38 ± 2.74abcde 6.68 ± 0.06b2.56 ± 0.03gh 1.66 ± 0.04e10.07f
Nidhauli 85.85 ± 3.41abcde 6.09 ± 0.05cd 2.4 ± 0.04i1.74 ± 0.09c10.06g
Environmental Science and Pollution Research
1 3
pH, and it decreases at low pH (Bharti etal. 2018). At a neu-
tral pH, the plant is immune to air pollutants; nevertheless,
at a low pH, it is prone to pollution.
RWC of leaf plays important role in whole metabolic
activities of crop plants like cell permeability, photosyn-
thetic activity, and enzymatic reactions. In our study,
results showed that all crops grown at polluted sites had
less RWC than the least polluted site. During 2016–2017,
we recorded maximum RWC in Trifolium alexandrinum
(92.26), whereas minimum in Brassica juncea (83.41)
at the least polluted site and Khangoda (most polluted
site), and maximum RWC was found in Triticum aestivum
(82.17), whereas minimum in Sorghum bicolor (62.79).
During second year study, we observed highest RWC in
Sorghum bicolor (90.70) and minimum in Brassica juncea
(85.42) at the least polluted site, whereas at the most pol-
luted site, maximum in Pennisetum glaucum (79.76) and
minimum in Sorghum bicolor (66.18). According to Rai
etal. (2013), Highest RWC was found at least polluted
site (87.23%) and lowest at the polluted site (63.08%). It
means higher air pollution level reduced the RWC in plant
leaves. Air pollutants, mostly particulate matter deposited
on crop canopy, affect the transpiration rate, which directly
relates to the water content of the plant leaves. Higher
pollution reduced the RWC, so that it affects the cell per-
meability, which results in more water loss. Plants with
higher RWC in their leaves are more tolerant of the air
pollution compared to the less RWC level. RWC aids the
plant’s ability to withstand drought stress (Pandey etal.
2016). Plants need a lot of water to keep their physiologi-
cal equilibrium when they are under stress, such as when
they are exposed to pollution (Lohe etal. 2015; Manjunath
and Reddy 2019). These air pollutants disturb the water
transportation from roots to the leaf hampering the rate of
transpiration, cooling of the leaf, and transfer of minerals
(Sen etal. 2017).
Table 6 Biochemical parameters and APTI values for different crop species at selected sites during 2017–18
Values in the same column (within same sub-table) followed by same letter are not significantly different according to Tukey’s Honest test
(p = 0.05)
Crops Sites RWC (%) pH Total chlorophyll (mg/g) Ascorbic acid (mg/g) APTI
Oryza sativa Akilpur Jagir 90.54 ± 2.98a6.77 ± 0.02c2.87 ± 0.09bc 2.07 ± 0.09a11.05c
Khangoda 79.15 ± 2.9cd 5.21 ± 0.02f2.46 ± 0.08bcd 1.11 ± 0.05c8.77k
Ranauli Latifpur 81.77 ± 2.73abcd 6.38 ± 0.02d2.55 ± 0.06cd 1.42 ± 0.03bc 9.45e
Nidhauli 85.44 ± 1.22abc 7 ± 0.03b2.6 ± 0.07cd 1.40 ± 0.06bc 9.89d
Pennisetum glaucum Akilpur Jagir 89.58 ± 3.15ab 6.97 ± 0.04b3.18 ± 0.11a2.30 ± 0.06a11.29b
Khangoda 79.76 ± 2.52cd 5.76 ± 0.02e2.34 ± 0.08def 1.42 ± 0.04c9.13i
Ranauli Latifpur 80.65 ± 4.53bcd 5.84 ± 0.03e2.5 ± 0.05cde 1.65 ± 0.08b9.44f
Nidhauli 80.82 ± 4.31bcd 5.8 ± 0.06e2.59 ± 0.03cd 1.54 ± 0.07bc 9.37h
Sorghum bicolor Akilpur Jagir 90.7 ± 1.96a7.41 ± 0.06a2.88 ± 0.07b2.17 ± 0.05a11.30a
Khangoda 76.08 ± 2.34e5.84 ± 0.03e2.08 ± 0.07f1.30 ± 0.06bc 8.64l
Ranauli Latifpur 75.98 ± 2.23d6.4 ± 0.05d2.35 ± 0.08def 1.56 ± 0.07b8.96j
Nidhauli 82.62 ± 3.21abcd 6.23 ± 0.03d2.22 ± 0.05ef 1.35 ± 0.08bc 9.40g
Triticum aestivum Akilpur Jagir 86.15 ± 2.91abc 6.45 ± 0.02e3.45 ± 0.11a2.65 ± 0.05abc 11.24b
Khangoda 73.64 ± 3.33e5.41 ± 0.01m 2.7 ± 0.07def 1.17 ± 0.04d8.31p
Ranauli Latifpur 86.14 ± 3.07abc 6.6 ± 0.04d2.82 ± 0.08d1.34 ± 0.06cd 9.88f
Nidhauli 84.88 ± 2.5abc 6.02 ± 0.02hi 2.77 ± 0.06de 1.62 ± 0.07abcd 9.91e
Hordeum vulgare Akilpur Jagir 87.2 ± 3.71ab 6.84 ± 0.05c3.22 ± 0.1b2.44 ± 0.03a11.17c
Khangoda 77.53 ± 2.84cde 5.68 ± 0.05k 2.42 ± 0.07h 1.59 ± 0.08bcd 9.04m
Ranauli Latifpur 82.86 ± 3.47abcd 6 ± 0.03i2.67 ± 0.04f1.62 ± 0.05abcd 9.69h
Nidhauli 79.5 ± 3.94bcde 6.32 ± 0.02f2.7 ± 0.06ef 1.65 ± 0.07abcd 9.44k
Brassica juncea Akilpur Jagir 85.42 ± 2.33abc 6.92 ± 0.08b3.05 ± 0.04c1.82 ± 0.05abcd 10.36d
Khangoda 74.49 ± 1.46de 5.6 ± 0.05l 2.49 ± 0.07h 1.45 ± 0.08bcd 8.62o
Ranauli Latifpur 77.63 ± 3.33cde 5.86 ± 0.06j2.5 ± 0.08gh 1.47 ± 0.06bcd 8.99n
Nidhauli 80.09 ± 3.63bcde 6.08 ± 0.03h 2.67 ± 0.11ef 1.65 ± 0.05abcd 9.45j
Trifolium alexandrinum Akilpur Jagir 90.28 ± 3a7.32 ± 0.01a3.3 ± 0.04a2.27 ± 0.07ab 11.44a
Khangoda 79.54 ± 3.74bcde 6.24 ± 0.04g 2.08 ± 0.04i1.54 ± 0.03bcd 9.24l
Ranauli Latifpur 83.06 ± 3.83abcd 6.51 ± 0.02d2.6 ± 0.06d1.62 ± 0.08abcd 9.78g
Nidhauli 81.74 ± 3.36abcde 5.97 ± 0.013i2.38 ± 0.05h 1.67 ± 0.02abcd 9.56i
Environmental Science and Pollution Research
1 3
Total chlorophyll content in different crop species was
analyzed at selected sites and results showed that maxi-
mum total chlorophyll was in Trifolium alexandrinum
(3.42mg/g), whereas minimum in Oryza sativa (2.92mg/g)
at the least polluted site and Khangoda (most polluted site),
and maximum total chlorophyll was found in Triticum aes-
tivum (2.71mg/g), whereas minimum in Sorghum bicolor
(2.14mg/g) in 2016–2017. During second year study, we
observed highest total chlorophyll content in Triticum aes-
tivum (3.45mg/g) and the minimum in Hordeum vulgare
(2.87mg/g) at the least polluted site, whereas at the most
polluted site, maximum in Triticum aestivum (2.70mg/g)
and the minimum in Sorghum bicolor (2.08mg/g). Chlo-
rophyll content of crop plants depends upon so many fac-
tors like the type of crop species, different environmental
factors, morphological characters, and stress like air pollu-
tion. Air pollutants like SO2 reduce the chlorophyll content
due to formation of H2SO4, which converts the chlorophyll
to phaeophytin (Mandloi and Dubey 1988; Rao and Leb-
lance 1966). The higher level of chlorophyll content shows
the tolerance of crops to air pollution, whereas lower-level
indicates sensitivity. Jyothi and Jaya (2010) reported that
in Mangifera indica tree species total chlorophyll content
was highest (7.54mg/g) at the least polluted site and lowest
at the most polluted site (5.04mg/g). Joshi and Chauhan
(2008) observed a similar type of results in the case of rice
crop. According to earlier studies, the production of reac-
tive oxygen species (ROSs) is the leading cause behind the
reduction in chlorophyll content of the plant leaves under the
polluted situations (Pathak etal. 2011). ROSs is tiny reac-
tive molecules that can damage cell structures during stress
conditions. In this study, high levels of NO2, ozone, and
particulate matter may have caused chloroplast denatura-
tion, resulting in a decrease in chlorophyll content in pol-
luted sites compared to the control site (Gupta etal. 2016).
Because of leaf age, species variety, pollution levels, and
changes in biotic and abiotic circumstances, chlorophyll con-
centrations can vary (Katiyar and Dubey, 2001).
The ascorbic acid level was reduced at all polluted sites
compared to the least polluted site. Ascorbic acid was maxi-
mum in Hordeum vulgare (2.54mg/g) and the minimum in
Brassica juncea (1.74mg/g) at least polluted site, whereas,
maximum in Hordeum vulgare (1.62mg/g) and minimum in
Oryza sativa (1.27mg/g) at polluted site during 2016–2017.
The second-year study results showed minimum ascorbic
acid found in Brassica juncea (1.82mg/g) and maximum in
Triticum aestivum (2.65mg/g) at least polluted site, whereas,
minimum in Oryza sativa (1.11mg/g) and maximum in Hor-
deum vulgare (1.59mg/g) at a polluted site. When oxidative
air pollutants enter into the leaves of the plants, it disturbs
the chain reactions due to generation of free radicals. So
ascorbic acid content is decreased to remove these free radi-
cals. Plants with high ascorbic acid content are tolerant of
any stress as compared to the low level (Pandey and Agarwal
1994). According to Rai (2013), highest ascorbic acid was
found at the least polluted site and lowest at a polluted site
in different tree species.
Scavenging of ROSs might be an effective strategy to
control these molecules. Ascorbic acid in the plant leaves
plays the role of scavenger to protect the cell membranes
from oxidative damage caused by ROSs under such polluted
condition (Tambussi etal. 2000; Smirnoff 1996). Ascorbic
acid plays significant role in the defense mechanism of the
crop plants. An increased level of ascorbic acid in the leaves
indicates higher pollution tolerance of the plants to air pol-
lutants. On the side, some authors reported that low level of
ascorbic acid might be attributed to its role in the removal
of ROSs or free radicals generated due to reactions of pol-
lutants in the plant’s leaves (Rai and Panda, 2014).
Based on these biochemical parameters above, APTI was
calculated, and the results showed that crop grown at pol-
luted sites had less APTI value than the least polluted site.
The maximum APTI value was found in Trifolium alexan-
drinum (9.45) and the minimum in Brassica juncea (8.73)
at the most polluted site, whereas, at least polluted site
maximum APTI value was found in Trifolium alexandri-
num (11.79) and the minimum in Brassica juncea (10.03)
crop in 2016–2017. During 2017–2018, maximum APTI
value was found in Trifolium alexandrinum (11.44) and the
minimum in Brassica juncea (10.36) at the most polluted
site. Therefore, all APTI values were less than 12, which
mean all crops were sensitive to air pollution in the selected
area. Still, Trifolium alexandrinum crop was less sensitive
compared to other crops during first and second-year study,
respectively. Crop plants having more APTI value are tol-
erant, whereas those having less value are sensitive to air
pollution. Joshi and Chauhan (2008) also reported similar
types of results for APTI in rice crop. Tak & Kakde (2020)
examined the APTI of twelve plants in three industrial loca-
tions and three control sites, and discovered that the APTI of
all plants is lower in industrial areas than in the control site.
Javanmard etal. (2020) investigated four deciduous plant
species in Iran and found that the APTI of all four species
decreased as dust accumulation increased. These data back
with our findings, which showed that APTI was lower near
the power plant than at the control site. According to Molnar
etal. (2020), the APTI of Celtis occidentalis in industrial,
urban, and rural areas was 9.5, 14, and 15, correspondingly.
A significant decline in APTI value under the higher par-
ticulate matter deposition treatment as compared to control
in rice crop (Mina etal. 2018).
Anticipated performance index (API)
After the computation of APTI and API, we evaluated
the different crop species with respect to the air pollution
Environmental Science and Pollution Research
1 3
load. We found that maximum API value was seen in Trit-
icum aestivum (7) followed by Oryza sativa (6), Pennise-
tum glaucum (5), and other crops have API value 4 during
2016–2017. Triticum aestivum was assessed as best and
Oryza sativa as excellent, while other crops were evalu-
ated as well. Whereas during 2017–2018, results showed
that the maximum API value was seen in Oryza sativa (6)
followed by Triticum aestivum (5), Pennisetum glaucum
(5), and other crops had API value 4 or less. Oryza sativa
was assessed as excellent and Triticum aestivum and Pen-
nisetum glaucum as very good, while other crops were
assessed as good and poor (Table7, Table8). It means
Triticum aestivum and Oryza sativa were best-suited
crops in the selected study area as compared to other
crops. API and APTI found to be beneficial in the selec-
tion of crop species in the polluted area. The anticipated
performance index (API) was calculated for different spe-
cies, and results showed that F. bengalensis, Mangifera
indica, Psidium guajava, Ficus religiosa, Artocarpus het-
erophyllus, and Lagerstroemia speciosa were evaluated as
the best-suited variety for plantation along the roadside
of the polluted area (Rai etal. 2013). Pandey etal. (2015)
and Javanmard etal. (2020) also found similar types of
results in respect to the evaluation of the performance of
plants in the umbrella of air pollution stress.
Linear regression analysis
Linear regression analysis showed significant effects
(p < 0.05) of pH (R2 = 0.71 and R2 = 0.68), RWC (R2 = 0.68
and R2 = 0.90), total chlorophyll content (R2 = 0.69 and
R2 = 0.71), and ascorbic acid content (R2 = 0.87 and
R2 = 0.89) on APTI during the Kharif and Rabi seasons,
respectively (Fig.3). The consistency over season in terms
of R2 value was found in case of ascorbic acid content. In
case of pH and RWC though R2 value > 0.68 but intercept
was non-significant (p > 0.05) for both the season for pH
and non-significant (p > 0.05) intercept for the Kharif season
only for RWC (Fig.3).
Correlation coefficient analysis
Pearson’s correlation coefficient analysis is presented in
Fig.4. It revealed a significant positive correlation between
biochemical parameters (pH, RWC, AA and Chlorophyll)
at p = 0.01, 0.001. The highest correlation coefficient
value observed between AA and total chlorophyll content
(r = 0.78) and lowest value observed between pH and total
chlorophyll content (r = 0.58). Similarly, a significant posi-
tive correlation between air pollutant parameters (ADCC,
NO2, SO2, and SPM) at p = 0.01, 0.001. The highest cor-
relation coefficient value observed between SPM and
NO2 (r = 0.96) and lowest value observed between ADDC
Table 7 Evaluation of crop species based on API during 2016–2017
Crop species APTI value Crop habitat Laminar size Laminar
texture
Economic value Grade API grades Category
Total % score
Oryza sativa + + + + + + + + + + + 11 84.62 6 Excellent
Triticum aestivum + + + + + + + + + + + + 12 92.31 7 Best
Pennisetum glaucum + + + + + + + + + + − 10 76.92 5 Very good
Sorghum bicolor + + + + + + + + + − 9 69.23 4 Good
Hordeum vulgare + + + + + + + + + 9 69.23 4 Good
Brassica juncea + + + + + + + + + 9 69.23 4 Good
Trifolium alexandrinum + + + + + + + − − − 8 61.54 4 Good
Table 8 Evaluation of crop species based on API during 2017–2018
Crop species APTI value Crop habitat Laminar size Laminar
texture
Economic value Grade API grades Category
Total % score
Oryza sativa + + + + + + + + + + + 11 84.62 6 Excellent
Triticum aestivum + + + + + + + + + + 10 76.92 5 Very good
Pennisetum glaucum + + + + + + + + + + − 10 76.92 5 Very good
Sorghum bicolor + + + + + + + + + − 9 69.23 4 Good
Hordeum vulgare + + + + + + + + + 9 69.23 4 Good
Brassica juncea + + + + + + + + + 9 69.23 4 Good
Trifolium alexandrinum + + + + + + + − − − 6 46.15 2 Poor
Environmental Science and Pollution Research
1 3
and SO2 (r = 0.51). But, a significant negative correlation
between biochemical parameters (pH, RWC, AA, and Chlo-
rophyll) and air pollutant parameters (ADCC, NO2, SO2,
and SPM) at p = 0.05, 0.01, 0.001. The highest negative cor-
relation coefficient value observed between pH and SPM
(r = − 0.83) and lowest value observed between total chloro-
phyll content and ADDC (r = − 0.44). Fur thermore, among
biochemical parameters, AA has highest correlation value
(0.93) with APTI and among air pollutants SPM has highest
correlation value (0.94) with AQI.
Linear regression (Fig.3) and Pearson’s correlation analy-
sis (Fig.4) showed a very high positive linear association (or
correlation) between APTI and ascorbic acid content, and its
correlation value does not impact much by seasonal variation,
i.e., consistent over the season. Hence, ascorbic acid content
was found to be the most efficient biochemical parameter asso-
ciated with air pollution tolerance index of plants and this find-
ing is well supported by many authors (Das and Prasad 2010;
Kaur and Nagpal 2017).
Fig. 3 Linear regression
analysis of individual variables
(biochemical parameters) with
APTI values. a, b chlorophyll
content (mg/g) vs APTI for the
Kharif and Rabi seasons. c, d
pH vs APTI for the Kharif and
Rabi seasons. e, f Relative water
content (%) vs APTI for the
Kharif and Rabi seasons. g, h
Ascorbic acid content (mg/g) vs
APTI for the Kharif and Rabi
seasons. * indicate regres-
sion coefficient significant (at
p = 0.05) and ns indicates non-
significant. r denotes Pearson’s
correlation coefficient between
biochemical parameters with
APTI
)ITPA(xednIecnarelotnoitullopriA
Kharif season Rabi season
Biochemical Parameters at Kharif and Rabi season
y = *2.5216x + *3.3095
r=0.83, R² = 0.693
0
5
10
15
20
0 0.5 1 1.5 2 2.5 3 3.5
Chlorophyll Content(mg/g)
y = *2.3028x + *3.5675,
r=0.84, R² = 0.7106
0
5
10
15
20
0 0.5 1 1.5 2 2.5 3 3.5 4
Chlorophyll Content(mg/g)
y = *1.2404x + 1.9402ns
r=0.84, R² = 0.7082
0
4
8
12
16
20
0123456789
pH
y = *1.4571x + ns0.8656
r=0.84, R² = 0.6812
0
4
8
12
16
20
012345678
pH
y = *0.1054x + ns1.1407
r=0.84, R² = 0.6787
0
4
8
12
16
20
020406080 100
Relave water content(%)
y = *0.1831x - *5.3577
r=0.83, R² = 0.9034
0
4
8
12
16
20
020406080 100
Relave water content(%)
y = *2.2487x + *6.2191
r=0.93, R² = 0.8683
0
5
10
15
20
0 0.5 1 1.5 2 2.5 3
Ascorbic acid(mg/g)
y = *2.4855x + *5.6197
r=0.94, R² = 0.885
0
4
8
12
16
20
0 0.5 1 1.522.5 3
Ascorbic acid(mg/g)
Environmental Science and Pollution Research
1 3
Principal component analysis (PCA) andhierarchical
cluster analysis (HCA)
The air pollutants that affect biochemical characteristics of
plant were grouped into a two principal component model
PC1 and PC2 using PCA that accounted for 97.81% and
97.79% of the total data variability during Kharif and Rabi
season, respectively. In Kharif season, the first PC (PC1,
variance of 86.93%) included the pollutants SO2, NO2,
and SPM while the second PC (PC2, variance of 10.88%)
included the pollutants ADCC which cause lesser air pol-
lution than the other pollutants (Fig.5a). For Rabi sea-
son, similar the results of Kharif season PC1 (variance
of 81.17%) included SO2, NO2, and SPM and PC2 (vari-
ance of 16.62%) included ADCC, but with a much lesser
amount of contribution to air pollution than Kharif season
(Fig.5b). Significant variations in air pollutant contents
across all the sampling sites over the season (Kharif and
Rabi) were observed (Fig.5c and d).
HCA through dendogram analysis grouped four sampling
sites into two homogeneous (air pollutant level) clusters sea-
son wise, viz. Kharif (Fig.5c) and Rabi (Fig.5d). On Kha-
rif season, dendogram analysis showed that site Khangoda
(with highest pollution level) belongs to cluster 1 and the
three remaining sites viz. Akilpur Jagir (lowest pollution
level), Ranauli latifpur, and Nidhauli belong to cluster 2.
This confirmed that the air pollution level in the three
sites of cluster 2 was not significantly different among them-
selves but significantly lower than Khangoda. Furthermore,
in Rabi season, dendogram analysis showed that the site
Akilpur Jagir (with lower pollution level) belongs to clus-
ter 1 and the three remaining sites viz. Khangoda (highest
pollution level), Ranauli latifpur, and Nidhauli belong to
cluster 2. This confirmed that the air pollution levels in the
three sites of cluster 2 were not significantly different among
themselves but significantly higher than Akilpur Jagir. The
dendogram analysis clearly identified the seasonal variation
of sites in the air pollution level.
Conclusion
The study results indicate that air pollutants from coal-based
TPP affect the biochemical parameters at the varying degree
in selected crop species. Air pollutant level was maximum
at Khanhoda (most pollutes site) and minimum at Akilpur
Jagir (least polluted site). Gaseous pollutants like, SO2, and
NO2 and particulate matter as aerial deposition on crop can-
opy were the main culprits. Main findings from this study
were that the polluted sites (Khangoda, Ranauli Latifpur
and Nidhauli) have more APTI values as compared to the
least polluted site (Akilpur Jagir). All APTI values are less
Fig. 4 Correlation coefficient
of between different parameters
like biochemical parameters
(pH, RWC, AA, and Chloro-
phyll), air pollutants (ADCC,
NO2, SO2, and SPM), APTI,
and AQI. *, **, and ***
indicate correlation coefficient
significant at p = 0.05,0.01 and
0.001, respectively
Environmental Science and Pollution Research
1 3
than 11, which mean, all crops were sensitive to air pollu-
tion in the selected area. Still, Trifolium alexandrinum was
less sensitive compared to other crops during the first- and
second-year study, respectively. On the basis of API, Triti-
cum aestivum and Oryza sativa were best-suited crops in
the selected study area as compared to other crops. There is
an urgent need to standardize the APTI value for the crops
and we are continuously monitoring the APTI value of the
major crops in the polluted area since 2016. This kind of
study must continue on the different polluted sites to justify
the suitability of crops, according to their sensitivity and
tolerance level.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s11356- 022- 19505-3.
Acknowledgements The author (Lal Chand Malav) is thankful to the
ICAR-IARI, New Delhi, for providing the Senior Research Fellow-
ship (SRF) under the guidance of Professor Shakeel Ahmad Khan.
The financial and technical support for this work has been provided by
the Head, Environmental Sciences Division and the Dean, PG School,
ICAR-Indian Agricultural Research Institute, New Delhi, is also grate-
fully acknowledged.
Author contribution All authors contributed to the study conception
and design. Material preparation, data collection, and analysis were
performed by Shakeel A. Khan (SAK), Lal Chand Malav (LCM) and
Fig. 5 Principal component analysis (PCA) represents contribution of
air pollutants like ADCC (mg/cm2), NO2, SO2, and SPM concentra-
tions (μg/m3) on PC1 and PC2 during a Kharif season and b Rabi
season. Dendogram obtained by hierarchical cluster analysis (HCA)
of different sampling sites based on PCA using the parameters viz.
NO2, SO2, and SPM concentrations (μg/m3) during c Kharif and d
Rabi seasons
Environmental Science and Pollution Research
1 3
Sandeep Kumar (SK), Saidikul Islam (SI), and Priya Chaudhary (PC).
The first draft of the manuscript was written by Lal Chand Malav
(LCM) and all authors commented on previous versions of the manu-
script. All authors read and approved the final manuscript and revised
the manuscript critically for important intellectual content.
Funding This research work was financially supported by ICAR-Indian
Agricultural Research Institute, New Delhi, India. The authors declare
that no funds, grants, or other support were received during the prepa-
ration of this manuscript.
Availability of data and materials The authors confirm that the data
supporting the findings of this study are available within the article.
Declarations
Ethics approval On behalf of all authors, I, Shakeel A. Khan, ensure
that all the authors mentioned in the manuscript have agreed for author-
ship, read and approved the manuscript, and given consent for sub-
mission and subsequent publication of the manuscript. The order of
authorship has been agreed by all named authors prior to submission.
I am the corresponding author, who takes full ownership for all the
communication related to the manuscript.
Consent to participate I understand that I am free to contact any of
the people involved in the research to seek further clarification and
information.
Consent for publication I, Shakeel A. Khan, hereby declare that I par-
ticipated in the study and in the development of the manuscript title
“Assessing the environmental impact of air pollution on crops by moni-
toring air pollution tolerance index (APTI) and anticipated performance
index (API).” I and all the authors have read the final version and gave
consent for the article to be published in Environmental Science and
Pollution Research.
Competing interests The authors declare no competing interests.
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