Influence of meteorology, mobility, air mass transport and biomass burning on PM
2.5of three North Indian cities: Phase-wise analysis of the COVID-19 lockdown
M. Arunkumar1,* and S. Dhanakumar2
1Senior Research Fellow, Department of Environmental Science, PSG College of Arts and Science, Coimbatore, Tamilnadu, India-641014. E-mail: m.arunkumarmail@gmail.com
2Assistant Professor and Head, Department of Environmental Science, PSG College of Arts and Science, Coimbatore, Tamilnadu, India-641014. E-mail: ecodhanan.phd@gmail.com
Corresponding Author:
M. Arunkumar
Senior Research fellow,
Department of Environmental Science, PSG College of Arts and Science, Coimbatore, Tamilnadu, India-641014.
Contact No. +91 76393 60060
E-mail: m.arunkumarmail@gmail.com ORCID ID: 0000-0002-3381-2633
Supplementary Material 1: PM2.5 and Meteorological data collection
The daily average (24-h) concentrations of PM2.5 meteorological parameters (temperature, wind speed, wind direction, and relative humidity) for 2019 and 2020 were obtained from the web portal of the Central Pollution Control Board (CPCB) for the dissemination of air quality data (https://app.cpcbccr.com/ccr/#/caaqm-dashboard- all/caaqm-landing). Details of the Continuous Ambient Air Quality Monitoring Stations (CAAQMS) of the study cities and their geographical coordinates are provided in Supplementary Table S1. These stations continuously monitor criteria air pollutants (SO2, NOx, CO, PM10, PM2.5, O3, and lead) together with meteorological parameters.
Monitoring data is accessible to both public/institutional bodies and individuals. PM2.5 was measured based on the β- ray attenuation technique in which particulate matter was sampled through the instrument and collected on fiber- glass filter tape. β-ray radiation is measured before and after sampling by scintillation or Geiger–Muller counter (CPCB, 2019). The lower detection limit of monitors is 0.1 μg/m3. To check the data quality, operators often standardize the monitors in accordance with the device manual (George et al., 2019; Hama et al., 2020). From the daily mean values of the respective measuring stations in each city, a valid aggregated value (24-hour mean concentration at city level) was calculated and used for subsequent calculations. The outliers from each data set were removed for both the extreme lower and upper percentiles that were out of trend. Some missing data was observed in the continuous time-series data, which could be due to long power outages and maintenance that were discarded from the analysis. We used daily mean concentrations to compare with the meteorological factors that also used
Supplementary Material 2: PM2.5 and Meteorological Data Analysis
The data obtained were statistically treated using the SPSS software version 21.0 and data analysis tool from Microsoft excel-2016. To detect and estimate the changes within the time-series and quantify the lockdown effect on air pollutant levels, the study period was subdivided as listed in Supplementary Table S2. The legislative measures issued by the Government of India were taken into account for this grouping. Descriptive statistics and frequency analysis were used to explain daily and phase-wise trends of PM2.5. The deviations in the pollutant concentrations were computed by calculating the relative variation (in %) and the difference in the mean concentration (in μg/m3) between the lockdown phases. PM2.5 level in lockdown phases (of 2020) compared with the concentrations observed during the same periods of the previous year (2019). Data before 2019 were not included in the present investigation due to large inconsistencies. The statistical analysis is carried out with the statistical software IBM SPSS (Version 21.0. Armonk, New York, USA) in order to check whether there is a significant difference in the PM concentration. One-way analysis of variance (ANOVA) is often used to compare the mean of two or more groups. In this study, the daily PM concentration of different phases of lockdown is used as input to the one-way ANOVA test to see if there is a statistically significant difference in their means. Although the ANOVA results indicate an overall difference, a post-hoc test should be used to determine exactly where the difference is in the mean. Therefore, the Tukeys post-hoc test is carried out in addition to the ANOVA. A p-value < 0.05 is considered as the indication of significant variation between the groups. The Pearson correlation test was used to determine the correlation between PM2.5 and meteorological factors.
References:
CPCB. Continuous Stations Status, Central Control Room for Air Quality Management - All India. In: Cent. Pollut.
Control Board, Gov. India. https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqm-landing. Accessed 3 March 2021
CPCB. (2019). Technical Specifications for Continuous Ambient Air Quality Monitoring (CAAQM) Station (Real- Time). Central Pollution Control Board East Arjun Nagar, Shahdara, 2019; https://jspcb.
nic.in/upload/5d6f49fd8daebCAAQMSGuideline.pdf (accessed on 10 August 2020).
George, M. P., Sharma, S. K., Mandal, T. K., & Kotnala, R. K. (2019). Simultaneous measurements of ambient NH 3 and its relationship with other trace gases, PM 2.5 and meteorological parameters over Delhi, India. MAPAN, 34(1), 55-69.
Hama, S. M., Kumar, P., Harrison, R. M., Bloss, W. J., Khare, M., Mishra, S., ... & Sharma, C. (2020). Four-year assessment of ambient particulate matter and trace gases in the Delhi-NCR region of India. Sustainable Cities and Society, 54, 102003.
Table S1: Air quality monitoring stations, their locality, and coordinates
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City Stations Locality Coordinates
Ghaziabad
Loni Industrial 28.7334° N, 77.2986° E
Vasundhara Residential/Institutional 28.6624° N, 77.3734° E
Sanjay Nagar Residential 28.6940° N, 77.4550° E
Indirapuram Residential 28.6460° N, 77.3695° E
Noida
Sector - 125 Institutional 28.5438° N, 77.3310° E
Sector - 116 Residential 28.5672° N, 77.3970° E
Sector - 1 Industrial/Institutional 28.5900° N, 77.3116° E Sector -62 Residential/Institutional 28.6280° N, 77.3649° E Faridabad
Sector - 30 Residential 28.4417° N, 77.3217° E
Sector - 11 Institutional 28.3760° N, 77.3157° E
New Industrial Town Industrial 28.3907° N, 77.3006° E
Sector - 16A Residential/Institutional 28.4088° N, 77.3099° E
Fig. S1 Location of the study cities and Continuous Ambient Air Quality Monitoring Stations (CAAQMS)
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Phase Acronym Period Number of Days
Pre-Lockdown Pre-LD 1st January – 24th March 84
Lockdown 1 LD1 25th March – 14th April 21
Lockdown 2 LD2 15th April – 3rd May 19
Lockdown 3 LD3 4th May – 17th May 14
Lockdown 4 LD4 18th May – 31st May 14
Unlock 1 UL1 1st June – 30th June 30
Unlock 2 UL2 1st July – 31st July 31
Unlock 3 UL3 1st August – 31st August 31
Unlock 4 UL4 1st September – 30th September 30
Unlock 5 UL5 1st October – 31st October 31
Post-Lockdown Post-LD 1st November – 31st December 62
Table S2: COVID-19 Lockdown, Unlock, and Post-Lockdown phases, and its time period in India
Table S3: Statistical analysis of PM2.5 in different scenarios of lockdown
Phases Number of days
Mean ± Standard Deviation
Ghaziabad Noida Faridabad
Pre-LD 84 135.00 ± 66.77 122.50 ± 66.59 108.13 ± 62.94
LD 68 53.05 ± 25.16 44.99 ± 18.76 42.82 ± 24.09
UL 153 61.25 ± 52.99 59.21 ± 50.59 61.32 ± 44.90
Post-LD 61 248.36 ± 109.79 216.06 ± 97.58 168.43 ± 77.75
Table S4: Monthly descriptive statistics of temperature for the study cities during 2020
Month
Ghaziabad Noida Faridabad
Mean SD Min Max Mean SD Min Max Mean SD Min Max
January 31.29 0.31 30.83 32.31 29.95 0.56 29.28 31.49 29.13 0.63 28.16 30.52
February 31.26 0.16 30.94 31.61 29.25 0.58 28.15 29.98 32.82 2.34 28.77 36.79
March 31.24 0.26 30.80 31.74 29.56 0.53 28.78 30.77 24.67 5.24 12.72 42.72
April 30.76 0.33 29.91 31.24 28.54 0.44 27.57 29.02 26.38 2.39 15.18 28.45
May 30.77 0.74 29.28 32.74 28.75 0.51 27.73 29.65 29.52 2.71 26.00 34.37
June 29.77 0.48 28.76 31.10 28.73 0.75 27.52 29.82 29.32 4.41 14.63 33.83
July 28.32 1.42 25.75 30.48 28.45 1.14 26.09 30.07 29.90 2.79 17.05 32.91
August 28.02 1.46 26.44 33.73 30.91 1.50 26.94 33.00 29.08 2.45 21.51 32.06
September 28.43 0.81 27.08 29.69 28.51 0.64 26.92 29.74 27.09 4.35 13.86 31.21
October 28.73 0.35 28.15 29.60 29.15 0.81 28.44 31.55 27.49 1.99 21.29 30.65
November 28.92 0.44 27.89 29.78 30.60 0.54 29.16 32.03 24.42 2.09 15.80 29.21
December 28.86 0.20 28.50 29.32 31.38 0.59 29.93 31.96 22.39 1.55 18.96 25.19
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Table S5: Monthly descriptive statistics of Wind Speed (WS) for the study cities in 2020
Month
Ghaziabad Noida Faridabad
Mean SD Min Max Mean SD Min Max Mean SD Min Max
January 1.85 0.63 0.90 3.50 1.63 0.42 0.96 2.54 0.95 0.43 0.30 1.67
February 2.07 0.71 1.16 3.92 1.84 0.64 1.12 3.79 2.21 0.89 1.10 4.59
March 2.21 0.66 1.02 3.82 1.93 0.49 0.96 3.22 1.28 0.44 0.57 2.83
April 2.22 0.69 1.21 4.02 1.84 0.54 1.11 3.00 1.43 0.38 0.63 2.26
May 2.71 1.27 1.49 6.43 2.16 0.69 1.39 4.36 1.51 0.48 0.97 2.97
June 1.83 0.55 1.10 3.50 2.05 0.51 1.35 3.53 1.51 0.33 0.87 2.61
July 2.22 0.66 1.10 3.81 2.24 0.57 1.29 3.25 1.32 0.18 1.05 1.73
August 5.92 3.59 1.06 13.13 2.11 0.78 1.27 4.43 1.14 0.29 0.69 1.93
September 1.57 0.61 0.86 3.68 1.39 0.24 0.86 1.85 0.89 0.20 0.53 1.27
October 0.95 0.26 0.53 1.59 0.94 0.27 0.48 1.57 0.81 0.19 0.53 1.20
November 0.94 0.33 0.52 1.84 0.63 0.14 0.42 0.92 0.83 0.25 0.51 1.39
December 1.03 0.35 0.55 1.75 0.45 0.22 0.30 1.09 0.85 0.25 0.48 1.33
Table S6: Monthly descriptive statistics of Relative Humidity (RH) for the study cities during 2020
Month
Ghaziabad Noida Faridabad
Mean SD Min Max Mean SD Min Max Mean SD Min Max
January 77.37 9.22 54.79 93.66 73.35 9.63 51.85 88.73 83.54 7.12 67.01 100.00
February 66.31 8.26 47.86 81.84 62.06 7.96 45.02 77.09 75.81 7.64 59.07 91.60
March 66.20 7.92 53.58 83.61 61.92 9.63 46.19 82.31 66.56 9.75 50.05 84.98
April 44.76 8.16 34.06 66.87 41.96 8.39 31.69 66.22 46.65 9.64 34.89 72.32
May 45.79 15.62 23.94 80.96 43.03 15.85 22.36 75.26 46.14 16.09 23.88 81.30
June 62.37 8.10 50.72 81.71 59.23 7.45 48.38 76.29 62.34 7.23 47.48 76.78
July 77.82 7.09 65.54 91.51 73.10 8.38 57.98 87.87 72.50 10.75 46.49 89.55
August 83.21 6.46 70.67 95.26 81.16 7.30 65.82 94.25 79.62 8.42 64.86 96.75
September 70.73 7.01 55.66 82.55 67.30 7.58 55.52 80.51 62.92 7.85 47.46 76.37
October 49.25 7.24 35.00 64.66 42.68 9.44 26.34 60.12 43.60 6.19 32.94 57.71
November 55.57 8.29 38.39 75.41 48.08 9.78 30.65 75.40 51.90 9.06 34.69 76.30
December 68.72 9.96 44.82 90.21 59.33 11.38 33.65 88.57 60.96 8.84 40.57 79.01
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Fig. S2: Day-wise number of fire incidents and Cumulative Fire Radiative Power (MW) for 2020. The colored area represents the period with a high fire incidence rate and FRP emissions.
