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The area under investigation is Patna, the capital and largest city of Bihar situated 15 km along the bank of River Ganga between 25°56′N to 25°69′N latitude and 85°02′E to 85°25′E longitude in the Indo-Gangetic Plains. The city experiences a subtropical climate with a hot summer, a cold winter, and heavy rainfall during the monsoon season. Relative humidity can reach up to 100% during the summer season. At present, the Bihar State Pollution Control Board (BSPCB) monitors the air quality of Patna with the help of 6 Continuous Air Monitoring Stations (CAMS) distributed throughout the city (Fig. 1).

Fig. 1. 
Map showing the study area along with monitoring stations.

To study the effect of lockdown on the air quality of Patna, primary data from 6 different monitoring stations was collected from the CPCB online portal (https://app.cpcbccr.com/AQI_India/) and the BSPCB online portal (http://bspcb.bih.nic.in/environment-monitoring-data.html). The concentration of 7 different air pollutants was considered, including PM_2.5, PM₁₀, NO₂, NH₃, SO₂, O₃ and CO. For contaminants such as PM_2.5, PM₁₀, NO₂, NH₃ and SO₂, the daily average (24h) subindex value and CO and O₃ daily maximum (8h) average sub-index value had been taken. This was further analysed to see the change in their mean concentration between different phases. The fluctuation in AQI value and mean concentration have been studied in four phases in the year 2020 from pre-lockdown (1^st January to 24^th March) to lockdown (25^th March to 31^st May) to early phases of post-lockdown (1^st June to 31^st August) to the late phase of post-lockdown (1^st September to 15^th December). The method adopted to calculate NAQI is tabulated in (CPCB, 2014) briefly outlined here. AQI uses PM_2.5, PM₁₀, NO₂, SO₂, CO and O₃ as criteria pollutants, where the selection of parameters depends on AQI objective(s), data availability, averaging period and monitoring frequency (CPCB, 2014). Of all the air pollutants, the calculation of AQI requires a concentration of a minimum of three pollutants, with at least one being either PM_2.5 or PM₁₀. The maximum sub-index AQI_i of the corresponding pollutant is the overall AQI, where the sub-index AQI_i of each pollutant can be calculated using Eq. (1).

Where C_i is the concentration of pollutant ‘i’; B_HI and B_LO are breakpoint concentrations higher and lower than C_i and I_HI and I_LO are corresponding AQI values equivalent to B_HI and B_LO respectively. The health breakpoint of each pollutant is given in Table 2.

The minimum, maximum and mean value of the daily average concentration of different air pollutants have been summarized in Table 3. The results revealed particulate matter (PM_2.5 and PM₁₀) as the dominant and indicatory air pollutant during the entire study period. The concentration of PM₁₀ ranged from 17.33 μg/m³ to 382.12 μg/m³ while PM_2.5 was observed between 9.00 μg/m³ to 236.38 μg/m³ during the study period. The dominance of particulate matter among the air pollutants in the study area might be due to various anthropogenic activities like re-suspension of road dust, construction activities, open burning of solid waste, emission from vehicles and brick kilns etc. Patna is situated along the bank of river Ganga and ¾^th of its soil is alluvial and makes the soil soft and prone to the formation of dust which is blown into the city from the sandy riverbank adding to the particulate matter level as outside contributors (PCAAP, 2019). Various atmospheric reactions producing secondary particulate matter also add to the level of PM_2.5 and PM₁₀ in the study area. Fig. 2 represents the variation in daily average (24h) concentration of PM₁₀, PM_2.5, NO₂, SO₂, NH₃ and daily maximum (8h) average of O₃ and CO between 1^st January 2020 and 15^th December 2020 in the city of Patna. The result revealed a reduction in the daily average value of PM₁₀, PM_2.5, NO₂, SO₂, NH₃ and CO from pre-lockdown to lockdown and from lockdown to early phases of post-lockdown. The daily average PM₁₀ value was beyond the NAAQS standard limit of 100 μg/m³ during the pre-lockdown. The daily average concentration of PM₁₀ and PM_2.5 has been reduced by 39.57% and 59.79%, respectively, from pre-lockdown to lockdown (Table 3). A higher decrease in PM_2.5 value compared to PM₁₀ could be due to reduction in transport activities as the contribution of vehicular emissions and emissions from the combustion of gasoline or other such fuels to PM_2.5 is much more pronounced than that of PM₁₀ (PCAAP, 2019; ARAI and TERI, 2018; Guttikunda and Jawahar, 2014). Interestingly, the scenario gets reversed when the percent change between lockdown and the early stages of post-lockdown are analyzed. PM₁₀ declined by 49.49%, while PM_2.5 dropped only by 36.44%, which might be due to the relaxation in inter-state and intra-state travel in the early post-lockdown period as vehicular emission contributes more to PM_2.5 (Kumar et al., 2020b). Further, negligible construction, industrial activities, waste burning reduction, landfills, and intermittent rainfall might be responsible for a significant drop in PM₁₀ level during the early post-lockdown period (CARB 2021; Sahoo et al., 2021; Sathe et al., 2021; Kumar et al., 2020a). A decline in the average concentration of NO₂ and NH₃ was also observed. The NO₂ value can be seen at an alarming level during pre-lockdown and has decreased by 52.52% from pre-lockdown to lockdown and 18.74% from lockdown to initial unlock stages, mainly due to a reduction in the transport sector. The NH₃ daily average concentration has been reduced by 58.20% from pre-lockdown to lockdown. Although the anthropogenic source of NH₃ originates mainly from agricultural activities, including the use of soil fertilizer, domestic animal waste and the use of ammonia-based fertilizers, the reduction of NH₃ can be attributed to industrial and traffic emissions in the urban areas (Farren et al., 2020; Li et al., 2020; Wang et al., 2015). No significant change in SO₂ concentration has been observed during the entire study period except in early post-lockdown ( June to September) that suggests vehicular restriction might have not much impact on SO₂ concentration (Lokhandwala and Gautam, 2020). Decrease in SO₂ concentration during the early post-lockdown period is due to substantial rainfall during this period (Fig. 2). Several researchers reported a reduction in the concentration of SO₂ during the rainy season, probably due to the washout effect (Ngarambe et al., 2021; Xue et al., 2020). The result revealed that the average concentration of CO had declined by 24.04% during the lockdown, and it had further decreased by 40.54% during the early post-lockdown period. A decrease in CO concentration during the lockdown period might be due to restrictions on combustion engines, heating furnaces and automobile exhaust. Further decrease in CO concentration during the early post-lockdown period may be due to the precipitation washout effect (Ngarambe et al., 2021; Yoo et al., 2014). Strikingly, the mean average concentration of O₃ has increased by 73.75% from pre-lockdown to lockdown, which is due to a decrease in NO_x and VOCs concentration because of the complete restriction on industrial activity and vehicular movement (Filonchyk and Hurynovich, 2020; Kumari and Toshniwal, 2020; Mahato et al., 2020). Sharma et al. (2020) reported that a decrease in the concentration of particulate matter could result in more sunlight penetration, thereby increasing the photochemical reaction that can uplift O₃ production.

Fig. 2. 
Variation in criteria air pollutants level from pre-lockdown to post-lockdown in the year 2020.

The pollutant concentration data also revealed the effect of sequential unlock phases on air pollutants. PM_2.5 has experienced a maximum increase of about 235.01% from the early stages of post-lockdown to the late stages, followed by PM₁₀ (176.57%), NO₂ (137.29%), SO₂ (105.18%), CO (94.94%), NH₃ (87.36%) and O₃ (4.06%). An increase in the concentration of these pollutants during sequential unlock phases might have resulted from resumption of anthropogenic origin pollutants and changing weather and meteorological conditions (Mahato et al., 2020; Sharma et al., 2020; Srivastava et al., 2020).

The daily, weekly, and monthly variations of different air pollutants are conditionally formatted (Fig. 3). The higher value has been colored by red that decreases to green at the lower values. PM₁₀, PM_2.5, NO₂, NH₃ and CO remained high during the pre-lockdown (January to March) and late post-lockdown (October to December). During the lockdown, each of them experiences a reduction in their daily average concentration. The concentration of O₃ on the other hand, was observed to be high in May. The result suggests the reduction in transportation and industrial activities has a predominant impact on the concentration of particulate matter, NO₂, and NH₃, as their concentration started dropping with the implementation of the lockdown (near the end of March). CO and SO₂ have begun to decline at the tail end of April, suggesting restrictions on vehicular emissions might not have much to do with these pollutants, specifically SO₂, and incomplete combustion of fossil fuel from biomass burning might have contributed to CO. The concentration of NH₃ shows an unusual increase towards the end of August (Fig. 3). This increase was explicitly marked in the data collected from the Danapur site, which might be due to some agriculturally based activities (CGWB, 2015) in addition to vehicular emissions.

Fig. 3. 
Conditional formatting representing daily, weekly and monthly variation in air pollutants level in 2020.

The concentration of pollutants showed a further decrease in their daily average concentration during the early stages of the post-lockdown (1^st June to 31^st August). The effect of lockdown coupled with substantial monsoon rainfall, the contribution of clean air from the Bay of Bengal and Arabian Sea, and little input from brick kilns and biomass burning might be the reason behind such a drop (Arif et al., 2018). The diagrams also reveal a gradual increase in the deterioration of air quality from October onwards due to the relaxation in lockdown, the emergence of the winter season, prevailing westerly wind, and other anthropogenic factors that sum up to degrade air quality during the late phase of lockdown.

Fig. 4 depicts the spatial variation of different air pollutants in the study area. PM_2.5 showed a significant reduction during the lockdown phase at nearly every sampling site in the city. During the first week of lockdown, it was reduced to 47.22% from the pre-lockdown level. NO₂, on the other hand, has recorded maximum reduction of 63.18% within seven days since lockdown, suggesting restrictions on transport and other anthropogenic activities have played an essential role in lowering their concentrations. The concentration of NO₂ and CO was found to be greater at site 3 during the lockdown phase than that of the pre-lockdown phase, which might be due to biomass burning (Beig et al., 2020; Biswal et al., 2020; Ravindra et al., 2020) for household purposes by the population residing in two densely populated slum areas located near this monitoring site. An increase in NO₂ levels during the early phases of post-lockdown, especially in the western and central pockets of the study area (Fig. 4) might be due to relaxation in transport activity. Towards the end of the year, an increase in air pollutants was noticed owing to various anthropogenic and natural factors. Spatial variation revealed that pollutants load prevailed mainly in the city’s central region during the entire study period. The result is confined to the pollutants including PM_2.5, NO₂, and CO; the rest do not vary very much or are excluded due to the unavailability of data for some phases.

Fig. 4. 
Comparing the spatial distribution of air pollutants concentration during pre-lockdown, lockdown, early and late post-lockdown phases in Patna for the year 2020.

Correlation between different air pollutants was computed for the study period (Fig. 5). The correlation analysis revealed the daily average concentration of PM₁₀ to be very strongly correlated with PM_2.5 (r=0.87), which suggests they both contribute to the air from common sources. PM₁₀ and PM_2.5 showed moderate correlation with NH₃ (r=0.47; r=0.46), indicating a favorable role of ammonia conversion from gas to particle phase in particulate matter formation (Wang et al., 2015). The correlation of particulate matter with NO₂ (^r10=0.74, ^r2.5=0.63) and SO₂ (^r10=0.50, ^r2.5=0.46) suggests the presence of secondary pollutants generated by the photochemical reaction of gaseous pollutants mainly SO₂ and NO₂ (CARB, 2021; Botkin and Keller, 2000; Xu et al., 2020a). O₃ showed a negative correlation with all other pollutants, predominantly with PM_2.5, and NH₃ during our study period and similar findings were also observed by Kumari and Toshniwal, 2020; Mahato et al., 2020; Sharma et al., 2020; Xu et al., 2020b.

Fig. 5. 
Showing correlation between different air pollutants.

A comprehensive comparison of the concentration of five air pollutants (PM_2.5, NO₂, SO₂, CO, O₃) during pre-lockdown, lockdown and different phases of post-lockdown in the year 2020 was made with corresponding period in the year 2019 (Fig. 6). The result revealed that a significant impact of lockdown is noticeable when the deviation was evaluated from lockdown to early stages of post-lockdown (from 1^st June 2020 to 31^st August 2020). In 2020, all the air pollutants showed a negative % change where the maximum reduction was observed for SO₂ that has decreased from 8.79 μg/m³ to 4.05 μg/m³ (53.95%) in 2020, followed by CO (38.57%), PM_2.5 (36.38%), NO₂ (20.82%) and O₃ (19.37%). Compared with the previous year’s data, the monthly average concentration of NO₂, SO₂, CO was indeed higher during the same time (Fig. 6). Nearing the end of the study period, air pollutants have remarkably shown an increase in their concentration for both years. However, air pollutants have come across a more tremendous % increase in the year 2020 compared to 2019. The monthly average concentration of PM_2.5 has been maximum during the late phase of post-lockdown, which was noted at 88.70 μg/m³ in 2020 and 139.35 μg/m³ in 2019. Similarly, NO₂ has increased from 17.26 μg/m³ to 43.42 μg/m³ in the year 2020 and from 21.53 μg/m³ to 31.23 μg/m³ in the corresponding period of the year 2019. 43.18% and 113.70% increase can also be noticeable for CO in 2019 and 2020 respectively (Fig. 6). An increase in the concentration of these pollutants during this phase might be due to the lowering of mixing layer height hindering the complete dispersion of pollutants (Guttikunda and Jawahar, 2014), winter inversion (Mahato et al., 2020; Mishra and Kulshrestha, 2020). Increase in biomass burning during winter that contributes 18-30% of the total concentration level during winter (PCAAP, 2019), burning of crackers during festive and marriage occasions at the tail end of the year (Mahato et al., 2020; Chauhan and Singh, 2017) also increase the concentration of pollutants. Besides these factors, high biomass residue burning in Northwest countries and the Northern regions of India sums up with dust aerosol from the west during winter and pre-monsoon (Srivastava et al., 2020; Arif et al., 2018), resumption of brick manufacturing during winter and pre-monsoon period and westerly air mass coming from Indo-Gangetic Plain resulted in a comparatively elevated level of PM_2.5 and PM₁₀ during winters (Agarwal et al., 2020; Singh and Chauhan, 2020; Arif et al., 2018).

Fig. 6. 
Comparative analysis of percentage change in air pollutants level between 2019 and 2020.

Fig. 7 shows changes in the monthly average AQI value from 1^st January to 15^th December for 2019 and 2020. Since the beginning of the year, the air quality has improved slightly compared to the previous year. However, compared to the AQI value for the same period in 2019, the most significant drop in the AQI value in 2020 occurs from April to June. The AQI value ranged from 30 to 224 in 2020, peaking at 275 in 2019 with a 30.91% decline in the average AQI value during the lockdown period. The AQI value remained in a good to the moderately polluted zone during the lockdown and early post-lockdown phases that were in the poor category last year (Fig. 7). Compared to 2019, the AQI value this year has declined by 57.88% from pre-lockdown to lockdown, which was recorded as 47.57%. Such a drop in the AQI values in the pandemic and early stages of post-pandemic phases again justifies our discussion that the implementation of lockdown was a powerful reason for the changes observed in the air quality this year.

Fig. 7. 
Variation in AQI value between the years 2019 and 2020 at Patna.