Wet Deposition of Total Nitrogen, Dissolved Organic Carbon and Heavy Metals Investigating Role of Long-Range Transport at Two Sites in Delhi

doi:10.21203/rs.3.rs-3238470/v1

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Wet Deposition of Total Nitrogen, Dissolved Organic Carbon and Heavy Metals Investigating Role of Long-Range Transport at Two Sites in Delhi

https://doi.org/10.21203/rs.3.rs-3238470/v1

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Precipitation is one of the significant phenomena for various nitrogen, carbon and metal fractions depositions. In the current study, Total nitrogen (TN), Dissolved organic carbon (DOC) and metal concentrations were measured during different seasons in 2018 and 2019 to find out their potential sources at two sites in Delhi region having distinct land use pattern. The total nitrogen (TN) mean concentration was found to be 16 mg/l and 7 mg/l for DG and JN site respectively. Whereas the dissolved organic carbon (DOC) mean concentration was found to be 3.8 mg/l and 2.5 mg/l at DG and JN site respectively. The sequence for the metal concentrations was recorded as Ca>Na>K>Mg> Fe>Al>Cu>Mn>Zn>As for DG site whereas at JN site recorded different sequence i.e., Ca> Na> K>Mg>Fe>Al>Mn>Zn>Cu>As. Different source can be attributed to the influence of anthropogenic activities (agriculture, animal husbandry) on nitrogenous species and biomass burning on dissolved organic carbon species. The wind rose plots indicated that local and regional sources located in the north-western direction influenced the wet deposition of the species while air-mass back trajectory analysis implied the influence of air masses originating from the Bay of Bengal, This study bridges the data gap in the less studied part of the Delhi region and provides new information for policy makers to deal with pollution control.

Total nitrogen

Heavy metals

wet deposition and long-range transport.

Elevated food demand and population burst have influenced the global N cycle. This has further led to over production of reactive nitrogen since the times when Haber’s Bosch succeeded to produce ammonia artificially (Erisman et al., 2008; Fowler et al., 2013). Nitrogen plays a crucial role in biogeochemical cycle impacting ecological environment (Schlesinger et al., 2013; Vitousek et al., 1997; Galloway et al., 2004). Ecosystem productivity is impacted by the quantity of reactive nitrogen (reactive N) in the environment, in addition to various nutrients ranging from iron, phosphorus, and silica (Mills et al., 2004). Consequently, the carbon cycle, specifically the elimination of carbon dioxide (CO₂) from the atmosphere by terrestrial and marine ecosystems, and the food production are associated with nitrogen availability (Duce et al. 1983; Galloway et al. 2008). Reactive nitrogen deposition (wet or dry) may benefit ecological systems by providing fertilizer or degrade them because of the acidification and buildup of extra nutrients (Driscoll et al., 2003).

Through a variety of human-induced (such as industry, transportation, and domestic wood burning) and natural (such as soils, thunder and lightning, plants, microbes, and viruses) sources reactive nitrogen compounds get released into the environment as its oxidized or reduced inorganic (IN) or organic (ON) forms (Neff et al. 2002; Dentener et al. 2006; Galloway et al. 2008). Reactive nitrogen (Nr) species play an important role in atmospheric chemistry. The abundance of nitrogen oxides determines the ozone levels, apart from contributions in acid precipitation. The predominant neutralizing gas for the acidic compounds is NH₃. The nitric acid ranks second behind sulfuric acid as a major contributor to atmospheric acidity (Seinfeld and Pandis 2016). As a consequence, atmospheric chemistry is also affected by the scavenging of Nr species by the wet deposition.

Furthermore, quantifying past and future changes in atmospheric deposition of Nr, DOC and metals is necessary to assess the influence of N emissions on both the climate and the ecosystem (Kanakidou et al., 2016). If we look at the global scenarios, Latin America distinguishes apart as the part of the world with the greatest supplies of Nr by naturally occurring biological nitrogen fixation (BNF), which is equivalent to 25% of the world's Nr produced in terrestrial ecosystems (Galloway et al (2004). Although it was 5 times less than whatever occurs naturally in Latin America, the Nr production by BNF in cultivated plants that fix nitrogen (C-BNF) in 1995 was nevertheless equal to 16% of the global C-BNF. On the contrary, just 6% of the world's nitrogen emissions in 1995 originated from the combustion of fossil fuels, and only 4% of the world's nitrogen emissions were contributed from manmade N fertilizer (Galloway et al., 2004). In Indian scenario, fertilizer-N consumption grew from 1970 to 2010 by almost 11 times, contrasted to 3 times rise in crop uptake and a fourfold rise in reactive N (Nr) loss (Raghuram et al., 2020). Crop cultivation also provides N inputs through biological N-fixation (BNF), in addition to N-fertilizer. Based on a conservative assessment of BNF in Indian agriculture, grains provide 32% and grain legumes contribute 43%, comprising 5.20–5.76 Tg N, or around 9.5–10.6% of the worldwide agricultural BNF.

Additionally, as the demands of society for energy and transportation have grown due to economic development, Nr biased balance has become even more pronounced. Given the enormous assistance burden on the nation's the government's finances for fertilizer-N and the additional environmental burden, the urgency to consider the usefulness of the Nr challenge for India appears to be more of an economic nature. A comprehensive investigation to understand reactive nitrogen potential sources and provide mitigation to combat the associated issues that's beneficial to everyone (Abrol et al., 2017).

Wet deposition process is one of the main mechanisms for scavenging of pollutants from the atmospheric (Andreae and Rosenfeld, 2008; Guo et al., 2015; Pan and Wang, 2015; Charlson and Rodhe, 1982; Kulshrestha, 2013). There has been a great deal of research conducted on the atmospheric wet deposition of pollutants including heavy Nr species, carbonaceous species and metals (Singh et al., 2017; Kawamura et al., 2015; Pentalaki et al., 2018; Cheng et al., 2010; Katoch et al., 2023). The findings have implied that a wide range of variables, including the quantity of rainfall, local emission levels, and long-distance transportation of pollutants, have an influence on the concentrations and fluxes of different pollutants (Rodhe et al, 2002; Kim et al., 2000; Theodosi et al., 2010; Cong et al., 2010; Vuai and Tokuyama, 2011; Connan et al., 2013; Montoya- Mayor et al., 2013; Granet et al., 2002). In spite of the mixing effects from several anthropogenic activities and natural sources, the origins of the trace elements in atmospheric wet deposition are exceedingly complicated (Vuai and Tokuyama, 2011; Montoya- Mayor et al., 2013; Hara and Akimoto, 1993). It has been reported that the biogeochemical processes that govern the movement of these elements would be profoundly impacted by atmospheric wet deposition of trace elements, and the dual ecological consequences (nutrient and toxicity) on aquatic life could potentially have a negative influence on human health (Xing et al., 2017).

2.1 Site description

The sampling site was located in the east zone of Delhi region (28° 41' 2.0508'' N, 77° 18' 54.3384'' E) also known as the border (Delhi-Uttar Pradesh). In the eastern region of Delhi, India, there is a busy area called Dilshad Garden. It is well-known for its residential colonies, industrial hubs, and educational facilities and hence can be termed as urban industrial site. This site like many other places in Delhi, has heavy traffic because of the large number of cars and inadequate infrastructure. During rush hours, especially on weekdays when people are heading to work or school, traffic in Dilshad Garden may be rather difficult. The area's major thoroughfares viz. Grand Trunk Road and Dilshad Garden main road, most of the times see considerable traffic flow. Moreover, an extensive bottleneck at the crossroads next to the Dilshad Garden Metro Station causes substantial congestion in the neighborhood. Heavy-duty vehicle traffic is also evidently present in the surrounding area.

The other selected site was Jain Nagar (JN) which is a residential site located at the west zone of Delhi region (28°43'51"N, 77°3'12"E). The JN colony is very recently inhabited. Previously, inhabitants of the area were engaged in agricultural activities. Over a time, there have been several large-scale development projects, and the population has increased leading to varied sources of pollution. The area is devoid of concrete roads and appropriate sanitation is lacking. It is under construction till date.

2.2. Sample collection

The event-based rainwater samples were collected manually using bottle and funnel assembly during the years 2018 and 2019 at both the sites (Kulshrestha et al. 2003; Singh et al. 2017). The sample collection assembly was kept before the onset of the rain and were removed as soon as the rain stopped. Samples were collected in two ways- i). where the funnels and sample colleting bottles were acid washed with 5% HNO₃ solution and 1–2 drops of same acid were poured in the sampling bottles to avoid surface adsorption of any metal; ii).where the bottles were washed with mili-Q water and added a pinch of thymol in sampling bottles preventing the samples from any biological degradation (Gillett and Ayers 1991; Granat et al. 1992). A total of 24 samples at DG site and 23 at JN site were collected. These were kept in a refrigerator at 4 ^oC till the time analysis was completed.

2.3 Chemical analysis

The samples were analyzed for TN, DOC and metals. The TN and DOC analysis was performed by using TOC-TN analyzer (Shimadzu model-TOC-LCPH E200 ROHS). This instrument provides simultaneous analysis of TOC here referred as DOC and TN. Standard aqueous solutions of potassium hydrogen phthalate were used to calibrate for TC measurements while standard potassium nitrate aqueous solutions were used for TN measurements. Heavy metal determination was carried out by using ICP-OES technique (Agilent 5110). It is one method of optical emission spectrometry. When plasma energy is given to an analysis sample from outside, the component elements (atoms) are excited. When the excited atoms return to low energy position, emission rays (spectrum rays) are released and the emission rays that correspond to the photon wavelength are measured. The element type is determined based on the position of the photon rays, and the content of each element is determined based on the ray's intensity (Katoch et al., 2023).

3.1 Variation of Total Nitrogen (TN) and Total Dissolved Carbon

The mean concentration of TN was 16 mg/l at DG site having extreme range lying from 1 to 100 mg/l whereas the mean concentration of TN at JN site was recorded as 7 mg/l with a range from 1 to 44 mg/l as given in Table 1. Relatively higher nitrogen fractions at DG site as compared to JN site which can be attributed to the number of sources generation of human waste, animal husbandry and agricultural practices at the sampling site. According to Wu et al (2020), the wet scavenging is responsible for the elimination of TN from the ambient atmosphere.

Table 1

concentrations of different carbon and nitrogen fractions in precipitation at sampling sites.
	Dilshad Garden	Jain Nagar
Concentrations (mg/l)
	Mean± S.D	Mean± S.D
TN	16.0±32.0	7.0 ±11.0
TC	4.0±2.7	2.8 ±1.1
DOC	3.8±2.7	2.5 ±1.0
DIC	0.3±0.0	0.3 ±0.1

The concentration of different carbon fractions is also noted in the form of Total Carbon (TC), Dissolved Organic Carbon (DOC) and Dissolved Inorganic Carbon (DIC) in Table 1. The TC concentration ranged from 2 to10 mg/l with a mean value of 4 mg/l at DG site and nearly the similar pattern has been observed at JN site having mean concentration of 2.8 mg/l ranging from 1.6 to 4.5 mg/l. The DOC concentration ranged from 1.7 to 10 mg/l with a mean value of 3.8 mg/l at DG site while at JN site, the mean concentration of DOC was measured as 2.5 mg/l ranging from 1 to 4 mg/l. The probable sources of carbon fractions in the ambient air can be attributed to the fossil fuel burning, biomass burning (Schimel et al., 1996; Mishra and Kulshrestha., 2017; Sonwani and Kulshreshtha., 2019). It was reported that the inorganic carbon fraction remained the lowest at both the sites with 0.3 mg/l mean value indicating equal influence of crustal dust as the major fraction of IC is contributed by CaCO₃ of the atmospheric dust in the Indian region (Kulshrestha et al, 2003). TN/DOC ratio at DG was 4 while at JN 2.5 which indicated that due greater traffic and industrial activities at DG, the TN/DOC ratio was higher. These activities contribute NOx which is oxidized to NO3 while the heavy duty vehicles contribute NH3 which is dissolved in clouds as well during rain event, apart from sanitation and NH₃ emissions from solid wastes (Hui et al., 2003; Singh and Kulshrestha, 2012).

3.2 Variation of metal concentrations at sampling sites

The mean concentrations of metals along with the standard deviation at the DG and JN sites are mentioned in Table 2. The order of metal concentrations at the DG site was as Ca > Na > K > Mg > Fe > Al > Cu > Mn > Zn > As whereas it was as Ca > Na > K > Mg > Fe > Al > Mn > Zn > Cu > As at the JN site. The order is almost similar which indicate similar source influence or a regional representative nature of rain chemistry. This also indicated that the concentrations of metals in cloud water are almost same but the local below cloud scavenging is site specific. DG site has more anthropogenic emissions due to this reason, a significant difference is seen for Cu and Zn at DG site. Crustal metals are noticed in similar range at both the sites. However, very high Al at the JN site cannot be explained with the available source knowledge. The spatial distribution of heavy metal concentrations is a useful aid to assess the possible sources of enrichment and to identify hotspots with high metal concentrations (Sun et al., 2010; Zhao et al., 2014).

Table 2

Metal fraction at DG and JN site.
S.no	Metal	Mean (µg/l) Concentration ± S.D
S.no	Metal	DG site	JN site
1	Cu	481.4±1044.7	50.8±103.9
2	Mn	141.4±203.0	113.0±256.0
3	Zn	111.6±88.0	63.9±68.6
4	Al	763.5±1350.0	2198.8±5569.8
5	As	39.7±62.0	18.8±39.8
6	Ca	4207.0±4216.6	3744.0±3345.5
7	Fe	274.9±495.0	297.9±498.5
8	K	986.7±1137.6	1605.5±2587.0
9	Mg	864.0±931.5	1149.6±1126.2
10	Na	5288.5±6569.6	1821.8±2134.5

3.3 Correlation matrix of various metal fractions, nitrogen and carbon compounds at DG and JN site.

The Pearson’s correlation coefficient indicates the relationship between the species available in rain samples. In this method, to test the statistical significance of the evaluated correlation the p value is being used. It is observed that the values of p less than 0.05 stipulate statistically significant non-zero correlations at the 95% confidence level. The correlation matrix is calculated with the help of SPSS software (Başak et al., 2010). A strong correlation is found between heavy metals and dissolved organic carbon species at all the sites. The similar association has been observed between heavy metals and DOC in one of the studies done by Herngren et al., (2005).

The DG site has shown a good correlation of DOC and Cu with Mn and Al of r = 0.84, r = .071 and r = 0.83, r = 0.55 at a confidence level of 0.01 respectively (Table 3). Mn has shown a good correlation with Al with r = 0.78 at p = 0.01. The good correlation among carbon compounds and heavy metals indicates their identical source of influence. Similar observation has been comprehended by Başak et al., 2010). This correlation depicted there was mobilization and translocation of metals were taking place and there was a good affinity between DOC and heavy metals (Kalbitz et al., 1998).

Table 3

Correlation coefficients for heavy metals, nitrogen and carbon compounds at DG site.
TN	TN	DOC	Hg	Cu	Mn	Zn	Al	As
DOC	1
Hg	.48	1
Cu	− .06	.42	1
Mn	− .17	− .15	− .18	1
Zn	.05	.84**	− .32	.83**	1
Al	− .22	− .09	− .21	.26	.25	1
As	− .09	.71**	− .26	.55**	.78**	.38	1
TN	− .26	− .48	− .22	.04	.38**	− .03	.17	1

**Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

Table 4

**Correlation coefficients for heavy metals, nitrogen and carbon compounds at JN site.**
	TN	DOC	Hg	Cu	Mn	Zn	Al	As
TN	1
DOC	.26	1
Hg	− .05	− .02	1
Cu	.15	.5	.29	1
Mn	.17	.73**	.32	.88**	1
Zn	.22	.6*	.26	.45	.44	1
Al	.76	.65*	.3	.88**	.98**	.4*	1
As	.23	.4	− .05	− .2	-0.0	− .07	− .1	1

**Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

A resembling pattern with other site can be seen where DOC has shown a good correlation with Mn, Zn and Al with r = 0.73 at a confidence level of p = 0.01, r = 0.6 and r = 0.65 at a confidence level of p = 0.05 respectively (Table 4). Cu has shown a good correlation with Mn which can be an indication of automobile exhausts and local transport traffic, Dao et al., 2014) has reported the similar situation. Al has showed the strong positive correlation with Cu and Mn having r = 0.88 and r = 0.98 at a confidence level of p = 0.01 respectively. Mn showed a strong positive correlation with Al with r = 0.98 at p = 0.01. The good correlation among trace metals such as Al, Cu, Mn and Zn depicted their source of origin from the dissolution of wind-blown soil particles and emissions coming from the road transportations can also cause the atmospheric pollution (Vuay et al., 2011).

3.4 Local source influence

As mentioned in prior research, local and regional fluctuations have a substantial impact on air pollution transport affecting below cloud scavenging (Sonwani and Kulshrestha, 2019; Misenis et al., 2006). We plotted the wind roses to this effect (Figs. 2& 3). At DG site, the resultant wind direction was mostly north-west and from South-east during the monsoon period (Fig. 2). At JN site, it was slightly different which has influenced the rain chemistry through below cloud scavenging raising the levels of TN, DOC and metals at the DG site as discussed in the sections 3.1 and 3.2.

3.4. Long-range transport using air mass back trajectory analysis.

The air mass back trajectories were calculated by the NOAA HYSPLIT model (Hybrid Single-Particle Lagrangian Integrated Trajectory Model) (Draxler and Rolph, 2003) to determine the major pathways of reactive nitrogen, carbon and metals fractions reaching to the receptor site. Air mass back trajectories were calculated for 500 and 1500 m above ground levels (MAGL). for 2018 and 2019 (Figs. 4 and 5 respectively). Air masses originating from the Arabian Sea (AS), Bay of Bengal (BB), and southern Asia (SA) exhibited distinct characteristics of the pollutants. It was reported that the air masses coming from bay of Bengal were more contaminated than the Southern Asia air masses (Arub et al., 2020). Through the winter, the prevailing winds originated from the north-western region of the Indo-Gangetic plain, which is known for emitting strong winds that might be responsible for bringing additional airborne pollutants to local sources were identified as biomass-burning, thermal power plant emissions, industrial emissions, and vehicular emissions (Kumar et al., 2016). It is concluded that the north-western direction is the most dominant direction indicating the emissions from Pakistan and Afghanistan and the sources enroute govern Delhi’s pollutants characteristics. Due to varied nature of sources and pathways, pollutant properties may vary (Arub et al., 2020).

Wet deposition is one of the important removal processes which brings down most reactive soluble N forms, organic carbon forms and associated metals to the natural surfaces. Considering their importance over highly populated sites of Delhi region, the present study was conducted at two sites to measure the concentrations of total nitrogen, dissolved organic carbon and metals in rain water during monsoon season. Study has revealed that there is a large variation in the concentration of TN, DOC along with the metal fractions at both the sites. The levels of TN, DOC and metals were higher at the DG site. Fractions of TN, DOC and metals are associated with local as well as transported sources from northwestern regions. The DOC can be attributed to the fossil fuel and biomass burning. Higher Zn and Cu at the DG site indicated that the road transport and industries as local sources had significant impact on rain chemistry. TN was noticed mostly associated with heavy duty traffic, industries, municipal solid wastes and human population density related sources and was higher at the DG site. TN/DOC ratio was also very high at DG site suggesting that local sources of nitrogen mostly NOx and NH3 are dominated at the DG site. Most of long range transport was associated with the airmasses coming from the Bay of Bengal. Since, TN, DOC and dissolved carbon and metals play a significant role in the atmospheric budget assessment; hence, it is important to consider the findings of this study while preparing deposition budgets of the region.

Acknowledgements Analytical support from Central Instrumentation Facility, School of Environmental Sciences, Jawahar Lal Nehru University, New Delhi, is acknowledged. Authors are also thankful to NOAA Air Resources Laboratory and Website (http://www.arl.noaa.gov/ready/hysplit4.html) for trajectory datasets. This work is part of DRS-India Network of Prof U C Kulshrestha.

Author’s contribution Sunaina: Sample collection and analysis, interpretation of data, drafting of manuscript

Umesh Chand Kulshrestha: Conception and study of design of the study; interpretation of data; critical revision of manuscript

Funding We sincerely thank the financial support received from UGC-SRF and DST-PURSE.

Data availability The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests The authors declare that they have no conflict of interest.

Ethical approval Not applicable.
Consent to participate Not applicable.
Consent to publish Not applicable.

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No competing interests reported.

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Wet Deposition of Total Nitrogen, Dissolved Organic Carbon and Heavy Metals Investigating Role of Long-Range Transport at Two Sites in Delhi

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Abstract

Figures

1. Introduction

2. Methodology

2.1 Site description

2.2. Sample collection

2.3 Chemical analysis

3. Results and discussion

3.1 Variation of Total Nitrogen (TN) and Total Dissolved Carbon

3.2 Variation of metal concentrations at sampling sites

3.3 Correlation matrix of various metal fractions, nitrogen and carbon compounds at DG and JN site.

3.4 Local source influence

3.4. Long-range transport using air mass back trajectory analysis.

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1