LitCovid-sentences  

PMC:7547912 / 1626-25319 JSONTXT 13 Projects

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Id Subject Object Predicate Lexical cue
T11 0-12 Sentence denotes Introduction
T12 13-121 Sentence denotes COVID-19 crisis is caused by coronavirus 2 (SARS-CoV-2), a severe acute respiratory syndrome (Jandrić 2020).
T13 122-275 Sentence denotes Currently, India is undergoing a 4.0 phase of confinement and has 190,649 confirmed COVID-19 cases and 5406 deaths until May 31, 2020 (covid19india.org).
T14 276-489 Sentence denotes Confinement in India or any part of the world ensures that all transportation, factories, construction work, restaurants, and other social places should be closed to follow the social distancing on a serious note.
T15 490-615 Sentence denotes These confinement phases not only help to control the spreading of infection, but also offer improvement in planetary health.
T16 616-723 Sentence denotes Air pollution is a major subgroup of environmental pollution which poses a serious threat to the ecosystem.
T17 724-879 Sentence denotes The risk of global sustainability can be reduced by controlling anthropogenic activities responsible for the emission of air pollutants in the environment.
T18 880-986 Sentence denotes India accounts for having one of the most polluted capitals and cities within the globe (Guttikunda et al.
T19 987-993 Sentence denotes 2019).
T20 994-1374 Sentence denotes During a study conducted by the Central Pollution Control Board (CPCB), the Ministry of Environment, India confirmed significant impact of 1-day confinement in the country (March 22, 2020), named as “Janata Curfew” of 14 h from 7 a.m. to 9 p.m., on air quality in terms of reducing pollutant level when compared with previous day data (Barkur and Vibha 2020) (source CPCB, India).
T21 1375-1778 Sentence denotes Keeping in view the above, in the present study, impact of COVID-19 confinement on air qualities among the populous site of four major metropolitan cities in India (i.e., site 1—ITO, Delhi; site 2—Worli, Mumbai; site 3—Jadavpur, Kolkata; and site 4—Manali Village, Chennai) were determined by evaluating alteration in PM2.5, PM10, NO2, NH3, SO2, CO, and ozone level from January 1, 2020 to May 31, 2020.
T22 1779-1948 Sentence denotes Pearson product-moment correlation coefficient (PPMCC)-based model analysis was also proposed which determine the impact of COVID-19 pandemic confinement on air quality.
T23 1949-2077 Sentence denotes Overall pandemic confinement has allowed the environment for detoxifying and renews itself in a lesser human interference phase.
T24 2078-2350 Sentence denotes Environmental analysts designate it as a silver lining in terms of decreased carbon and waste emission but recognize it as a flawed perspective due to the expectation that the AQI levels to return as the coronavirus vanish and in some cases, they could come back strongly.
T25 2352-2370 Sentence denotes Origin of data set
T26 2371-2502 Sentence denotes Air quality index (AQI) reports daily air quality and its elevated level is associated with public health risks (Szyszkowicz 2019).
T27 2503-2654 Sentence denotes Based on different national quality standards and dose-response relationships of pollutants, countries have different air quality indices (Zhang et al.
T28 2655-2673 Sentence denotes 2020; Sofia et al.
T29 2674-2680 Sentence denotes 2020).
T30 2681-2825 Sentence denotes The Indian national air quality index considers eight pollutants (PM10, PM2.5, NO2, SO2, NH3, CO, O3, and Pb) with a 24-hourly averaging period.
T31 2826-3046 Sentence denotes It is subdivided into six categories i.e., good (0–50), satisfactory (51–100), moderately polluted (101–200), poor (201–300), very poor (301–400), and severe (401–500) as shown in Fig. 1 (Perera 2018; Ghorani-Azam et al.
T32 3047-3053 Sentence denotes 2016).
T33 3054-3258 Sentence denotes The sub-indices for individual pollutants at a monitoring location are calculated using its 24-hourly average concentration value (8-hourly in case of CO and O3) and health breakpoint concentration range.
T34 3259-3345 Sentence denotes The worst sub-index is the AQI for that location (https://app.cpcbccr.com/AQI_India/).
T35 3346-3478 Sentence denotes An increment in AQI causes acute and chronic mode health concern especially in the older age people and in children (Januszek et al.
T36 3479-3496 Sentence denotes 2020; Pant et al.
T37 3497-3503 Sentence denotes 2020).
T38 3504-3640 Sentence denotes Due to the COVID-19 pandemic confinement, there is a significant reduction in the level of such toxic pollutants globally (Selvam et al.
T39 3641-3671 Sentence denotes 2020; Singh and Chauhan 2020).
T40 3672-3731 Sentence denotes Fig. 1 Indian national air quality index—category and range
T41 3732-4099 Sentence denotes In the present study, concentrations of different pollutants i.e., PM2.5 (diameter < 2.5 μm), PM10 (diameter < 10 μm), NO2, NH3, SO2, CO, ozone, and air quality index (AQI) were acquired from open access internet sources provided by the Central Pollution Control Board (CPCB), Ministry of Environment, Forests, and Climate Change (https://app.cpcbccr.com/AQI_India/).
T42 4100-4567 Sentence denotes The data were recorded daily from January 1, 2020 to May 31, 2020, which is subdivided into two groups: (a) pre-lockdown period—January 1, 2020 to March 23, 2020, and (b) lockdown period—March 24, 2020 to May 31, 2020 at 17:00 IST among four different air quality monitoring stations of the CPCB for four major metropolitan cities in India i.e., site 1—ITO, Delhi, site 2—Worli, Mumbai, site 3—Jadavpur, Kolkata, and site 4—Manali Village, Chennai as shown in Fig. 2.
T43 4568-4696 Sentence denotes For air quality assessment, % variations of air pollutants during the confinement period were compared with pre-lockdown values.
T44 4697-4808 Sentence denotes Fig. 2 The geography of monitoring stations among the populous sites of four major metropolitan cities in India
T45 4809-4893 Sentence denotes The air quality index is a piecewise linear function of the pollutant concentration.
T46 4894-4980 Sentence denotes At the boundary between AQI categories, there is a discontinuous jump of one AQI unit.
T47 4981-5493 Sentence denotes To convert from concentration to AQI, this equation is used:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$I=\frac{I_{\mathrm{high}}-{I}_{\mathrm{low}}}{C_{\mathrm{high}}-{C}_{\mathrm{low}}}\ \left(C-{C}_{\mathrm{low}}\right)+{I}_{\mathrm{low}}$$\end{document}I=Ihigh−IlowChigh−ClowC−Clow+Ilow
T48 5494-5663 Sentence denotes If multiple pollutants are measured, the calculated AQI is the highest value calculated from the above equation applied for each pollutant.whereIThe (air quality) index,
T49 5664-5693 Sentence denotes CThe pollutant concentration,
T50 5694-5738 Sentence denotes ClowThe concentration breakpoint that is ≤C,
T51 5739-5784 Sentence denotes ChighThe concentration breakpoint that is ≥C,
T52 5785-5832 Sentence denotes IlowThe index breakpoint corresponding to Clow,
T53 5833-5882 Sentence denotes IhighThe index breakpoint corresponding to Chigh.
T54 5883-6159 Sentence denotes Moreover, we have used unpaired Welch’s two-sample t test analysis to measure the statistically significant reduction in average AQI for all four sites, as t test allows us to compare the average values of the two data sets and determine if they came from the same population.
T55 6160-7922 Sentence denotes The formula for calculating t-statistics is given as:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t=\frac{{\overline{x}}_1-{\overline{x}}_2}{\sqrt{\frac{s_1^2}{n_1}-\frac{s_2^2}{n_2}}}$$\end{document}t=x¯1−x¯2s12n1−s22n2where, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\overline{x}}_1$$\end{document}x¯1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\overline{x}}_2$$\end{document}x¯2 are the sample means, n1 and n2 are the sample sizes, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${s}_1^2$$\end{document}s12 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${s}_2^2$$\end{document}s22 are the sample variances for samples 1 and 2 respectively.
T56 7923-8099 Sentence denotes To find out the most prominent pollutant concerning AQI statistically, we have done Pearson’s correlation analysis by the means of plotting heatmaps corresponding to each site.
T57 8100-8312 Sentence denotes Pearson’s correlation is also known as the “product-moment correlation coefficient” (PMCC) and is suitable for measuring the extent of the linear relationship between any two quantitative variables statistically.
T58 8313-8425 Sentence denotes A Pearson’s correlation is a number ranging between − 1 and + 1 showing negative to positive linear correlation.
T59 8426-9346 Sentence denotes Given a pair of random variables (X1, X2), the formula for Pearson’s correlation is given by\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\rho}_{X_1,{X}_2}=\frac{Cov\left({X}_1,{X}_2\right)}{\sigma_{X_1}\ {\sigma}_{X_2}}$$\end{document}ρX1,X2=CovX1X2σX1σX2where Cov(X1, X2) is the covariance between the variables under study and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\sigma}_{X_1},{\sigma}_{X_2}$$\end{document}σX1,σX2 are the standard deviation of X1, X2 respectively.
T60 9348-9356 Sentence denotes Analysis
T61 9358-9382 Sentence denotes Comparative study of AQI
T62 9383-9573 Sentence denotes In the present investigation, the AQI level was at its highest peak on year starting among all of the four studied sites i.e., 443 in site 1, 298 in site 2, 292 in site 3, and 166 in site 4.
T63 9574-9669 Sentence denotes Initial data indicates Delhi was in the hazardous range while poor air quality in other states.
T64 9670-9875 Sentence denotes Although irregular declining pattern was observed in the AQI level for all of the studied locations, a significant reduction within the pollutant level can be seen after comparing initial and final values.
T65 9876-10067 Sentence denotes A remarkable drop falls of 44%, 59%, 59%, and 6% in mean concentration of AQI which was observed during COVID-19 pandemic confinement for sites 1, 2, 3, and 4 respectively as shown in Fig. 3.
T66 10068-10363 Sentence denotes Fig. 3 Comparative AQI levels during pre-lockdown and lockdown period at 17:00 IST among four different air quality monitoring stations of the CPCB for four major metropolitan cities in India (site 1—ITO, Delhi, site 2—Worli, Mumbai, site 3—Jadavpur, Kolkata, and site 4—Manali Village, Chennai)
T67 10365-10400 Sentence denotes Comparative study of air pollutants
T68 10402-10419 Sentence denotes Site 1—ITO, Delhi
T69 10420-10572 Sentence denotes Delhi, India’s capital, is a massive metropolitan state in the northern area of the country and is among one of the most polluted capitals in the globe.
T70 10573-10713 Sentence denotes Due to overpopulation and other responsible factors for urbanization, the pessimistic anthropogenic impact on the environment is at maximum.
T71 10714-10883 Sentence denotes But, COVID-19 pandemic confinement facilitates the environment to retain its health which can be observed as a significant reduction in the air pollutant level in Delhi.
T72 10884-11151 Sentence denotes At site 1—ITO, Delhi, during confinement period, the mean concentrations of PM2.5, PM10, NO2, NH3, and SO2 significantly plummeted by 49%, 33%, 29%, 63%, and 24% respectively due to reduction in anthropogenic activities including traffic and manufacturing industries.
T73 11152-11304 Sentence denotes Besides, due to high temperature and insolation during the confinement period, mean ozone concentration was highly elevated by 109% as shown in Table 1.
T74 11305-11535 Sentence denotes Table 1 Air quality assessment—variations and change (%) of average concentrations for different air pollutants during the pre and COVID-19 pandemic confinement, 2020 among populous sites of four major metropolitan cities in India
T75 11536-11626 Sentence denotes Pollutants Pre-lockdown values Lockdown Variation and % change (pre-lockdown and lockdown)
T76 11627-11710 Sentence denotes Site 1 Site 2 Site 3 Site 4 Site 1 Site 2 Site 3 Site 4 Site 1 Site 2 Site 3 Site 4
T77 11711-11785 Sentence denotes AQI 238 151 144 68 134 62 59 64 − 104 (44%) − 89 (59%) − 86 (59%) − 4 (6%)
T78 11786-11864 Sentence denotes PM2.5 238 132 135 56 122 36 36 26 − 116 (49%) − 96 (73%) − 99 (73%) − 30 (54%)
T79 11865-11941 Sentence denotes PM10 150 116 122 60 100 61 45 49 − 50 (33%) − 54 (47%) − 77 (63%) − 10 (17%)
T80 11942-12007 Sentence denotes NO2 44 48 55 9 31 7 11 10 − 13 (29%) − 41 (86%) − 43 (79%) 1 (7%)
T81 12008-12069 Sentence denotes NH3 10 2 8 14 4 1 2 9 − 6 (63%) − 1 (58%) − 6 (74%) − 4 (30%)
T82 12070-12134 Sentence denotes SO2 19 12 11 14 14 5 9 9 − 4 (24%) − 7 (58%) − 2 (15%) − 6 (39%)
T83 12135-12200 Sentence denotes CO 53 28 33 25 84 13 22 35 31 (59%) − 15 (55%) − 11 (32%) 9 (37%)
T84 12201-12266 Sentence denotes O3 35 85 29 36 73 34 51 65 38 (109%) − 51 (60%) 22 (77%) 29 (80%)
T85 12267-12368 Sentence denotes PM2.5 in μg/m3, PM10 in μg/m3, CO in μg/m3, NH3 in μg/m3, NO2 in μg/m3, SO2 in μg/m3, and O3 in μg/m3
T86 12369-12390 Sentence denotes AOI air quality index
T87 12392-12412 Sentence denotes Site 2—Worli, Mumbai
T88 12413-12531 Sentence denotes Mumbai, the sixth most populous city in the world, is located on India’s west coast and is the capital of Maharashtra.
T89 12532-12599 Sentence denotes It is the financial, entertainment, and commercial center of India.
T90 12600-12750 Sentence denotes During COVID-19 pandemic confinement, the second most populated city of India i.e., Mumbai has moved from poor to a satisfactory level of air quality.
T91 12751-12933 Sentence denotes As initially at site 2, the values of the pollutants which were scattered around 200–300 μg/m3 before confinement fallen to less than 60 μg/m3 during the confinement period (Fig. 4).
T92 12934-13194 Sentence denotes The mean concentration of PM2.5, PM10, NO2, NH3, SO2, and CO, significantly reduced with a percentage of 73, 47, 86, 58, 58, 55, and 60 respectively due to shutdown of navigation activities and other industrial sectors with automobile transportation (Table 1).
T93 13195-13350 Sentence denotes The drastic decline in nitrogen oxide levels over Mumbai is the result of reduced carbon-emission hotspots, industrial and coal combustion-dominated areas.
T94 13351-13503 Sentence denotes A decrease in the concentration of urban ground-level ozone was recorded by 60% due to high reduction in nitrogen oxide concentration in the atmosphere.
T95 13504-13916 Sentence denotes Fig. 4 The concentration of air pollutants (PM2.5 in μg/m3, PM10 in μg/m3, CO in μg/m3, NH3 in μg/m3, NO2 in μg/m3, SO2 in μg/m3, and O3 in μg/m3) during pre-lockdown and lockdown period at 17:00 IST among four different air quality monitoring stations of the CPCB for four major metropolitan cities in India (site 1—ITO, Delhi, site 2—Worli, Mumbai, site 3—Jadavpur, Kolkata, and site 4—Manali Village, Chennai)
T96 13918-13942 Sentence denotes Site 3—Jadavpur, Kolkata
T97 13943-14029 Sentence denotes After Delhi and Mumbai, Kolkata is the third populous metropolitan area in the nation.
T98 14030-14159 Sentence denotes Kolkata is the educational, cultural, and commercial center of the eastern part of the country and is the capital of West Bengal.
T99 14160-14417 Sentence denotes The concentration of PM2.5, PM10, NO2, NH3, SO2, and CO at site 3 significantly dropped steeply from 242, 205, 85, 10, 9, and 49 μg/m3 as on January 1, 2020 to 20, 28, 9, 1, 7, and 22 μg/m3 during COVID-19 pandemic confinement on May 31, 2020, respectively.
T100 14418-14895 Sentence denotes Also, the mean concentration levels of PM2.5, PM10, NO2, NH3, SO2, and CO significantly reduced by 73%, 63%, 79%, 74%, 15%, and 32% due to decline in fossil fuel consumption, biomass burning, and other anthropogenic activities as observed from Fig. 4, while ozone levels were significantly raised by 77% with total variation of + 22 μg/m3 during confinement period as similar to Delhi due to high winds, intermittent rains and thunderstorms, and high temperature and heatwaves.
T101 14897-14927 Sentence denotes Site 4—Manali Village, Chennai
T102 14928-15090 Sentence denotes Chennai, the capital of Indian state of Tamil Nadu, is the fourth urban agglomeration in the nation and is the 36th largest urban area by population in the world.
T103 15091-15237 Sentence denotes It is located on the Coromandel Coast off the Bay of Bengal and is center for the cultural, economical, and educational activities of south India.
T104 15238-15422 Sentence denotes Similar to all other studied sites, the air quality of site 4—Manali Village, Chennai also confirmed improvement in terms of reduction in pollutant level during the confinement period.
T105 15423-15887 Sentence denotes The mean concentrations of PM2.5, PM10, NH3, and SO2 were reduced by 54%, 17%, 30%, and 39% respectively as shown in Fig. 4, while due to fuel and coal burning, vehicular emissions, and continuous functioning of power plants in the neighborhood of site 4, there was no significant reduction in NO2 (+ 1 μg/m3), CO (+ 9 μg/m3), and ozone levels (+ 29 μg/m3) (https://www.cag.org.in/blogs/air-quality-chennai-during-lockdown-do-we-have-clues-mitigate-air-pollution).
T106 15889-15917 Sentence denotes Pearson correlation analysis
T107 15918-16140 Sentence denotes The Pearson correlation coefficient was determined by constructing a heatmap for the concentration of various pollutants (pre and during pandemic confinement) among populous sites of four metropolitan cities of India, viz.
T108 16141-16215 Sentence denotes ITO, Delhi, Worli, Mumbai, Jadavpur, Kolkata, and Manali Village, Chennai.
T109 16217-16234 Sentence denotes Site 1—ITO, Delhi
T110 16235-16465 Sentence denotes At this site, the perfect positive correlation was observed between AQI and PM2.5, a strong positive correlation between AQI-PM10 and PM2.5-PM10, whereas a negative correlation was observed for ozone with AQI and other pollutants.
T111 16466-16639 Sentence denotes The correlation coefficient between AQI-PM2.5, AQI-PM10, and PM2.5-PM10 was found as 0.98, 0.82, and 0.77 respectively, showing a significantly higher positive relationship.
T112 16640-16823 Sentence denotes This indicate the changes in PM2.5 and PM10 concentrations have a great influence on AQI content; i.e., an increase in their concentration will directly elevate the air quality index.
T113 16824-17058 Sentence denotes Besides, AQI-ozone, PM2.5-ozone, and PM10-ozone confirmed low negatively correlated variables, i.e., − 0.31, − 0.38, and − 0.18 respectively indicating the higher values of AQI, PM2.5, and PM10 will lower down the ozone concentration.
T114 17059-17201 Sentence denotes A feeble correlation exists between AQI-NH3 (0.46), AQI-NO2 (0.38), AQI-SO2 (0.28), and AQI-CO (0.11) showing mild effect on AQI (Fig. 5 (a)).
T115 17202-17376 Sentence denotes Fig. 5 Pearson’s correlation heatmap for air pollutants during the pre and COVID-19 pandemic confinement, 2020 among populous sites of four major metropolitan cities in India
T116 17378-17398 Sentence denotes Site 2—Worli, Mumbai
T117 17399-17557 Sentence denotes Product-moment correlation coefficient analysis for site 2 demonstrates the positive correlation between all of the studied pollutants as shown in Fig. 5 (b).
T118 17558-17791 Sentence denotes The highest correlations were confirmed between AQI-PM2.5, with a correlation of 0.97, AQI-PM10, with 0.94, and PM2.5-PM10, with 0.91 which demonstrates PM2.5 and PM10 are the most significant dominating factors in elevating the AQI.
T119 17792-18019 Sentence denotes A correlation value of 0.80, 0.74, 0.72, and 0.86 between AQI-NO2, AQI-NH3, AQI-SO2, and AQI-CO indicates a significant positive relationship, while moderate correlation was determined between CO and ozone concentration (0.53).
T120 18021-18045 Sentence denotes Site 3—Jadavpur, Kolkata
T121 18046-18252 Sentence denotes A significant positive correlation was observed between the prominent pollutants PM2.5, PM10, NO2, NH3, and CO with AQI, i.e., 0.96, 0.95, 0.86, 0.70, and 0.70 respectively in site 3 as shown in Fig. 5 (c).
T122 18253-18542 Sentence denotes This implies the studied pollutants had a great impact on air quality among monitoring station of Jadavpur, Kolkata, whereas ozone shows a negative correlation with AQI (− 0.25), and other studied pollutants i.e., PM2.5 (− 0.32), PM10 (− 0.36), NO2 (− 0.48), NH3 (− 0.50), and CO (− 0.35).
T123 18543-18686 Sentence denotes This indicates mean O3 concentration will significantly increase with a decrease in the mean AQI, PM2.5, PM10, NO2, NH3, and CO concentrations.
T124 18688-18718 Sentence denotes Site 4—Manali Village, Chennai
T125 18719-18933 Sentence denotes Pearson’s correlation heatmap for Manali Village, Chennai demonstrates significant positive correlations for PM2.5 (0.69) and PM10 (0.73) with AQI, while other pollutants exhibit a moderate or negative correlation.
T126 18934-19187 Sentence denotes The lowest values of correlation coefficient were found for the pairs AQI-NO2 (0.26), AQI-NH3 (0.04), and AQI-CO (0.33) indicating mild association between these variables; i.e., the effect of concentration of NO2, NH3, and CO on air quality is minimal.
T127 19188-19409 Sentence denotes However, the approximately zero correlation between AQI-SO2 (0.009) and AQI-ozone (0.01) indicates no linear relationship, but there may be some other strong non-linear relationship between the two variables (Fig. 5 (d)).
T128 19410-19511 Sentence denotes In other words, we can say that the simple linear function cannot describe its relationship in depth.
T129 19513-19564 Sentence denotes Inferential t-statistic (Welch’s two-sample t test)
T130 19565-19737 Sentence denotes In the present study, the significant impact of COVID-19 pandemic confinement on air quality in studied locations was determined by right-tailed, Welch’s two-sample t test.
T131 19738-19915 Sentence denotes The complete data set was divided into two groups, pre-confinement (A) and during confinement (B) to assess if there is a statistically significant effect of confinement on AQI.
T132 19916-20062 Sentence denotes Independent random samples of sizes n1, n2 were drawn by using a random number table from both the groups and applied t test using the R-software.
T133 20063-20135 Sentence denotes This inferential statistic was used to test the following hypothesis:H0:
T134 20136-20272 Sentence denotes No significant difference between the means of two groups i.e., no significant effect of COVID-19 pandemic confinement on AQI (μ1 = μ2).
T135 20273-20276 Sentence denotes HA:
T136 20277-20483 Sentence denotes Significant difference between the means of two groups i.e., air quality is significantly improved during COVID-19 pandemic confinement (μ1 > μ2), where μ1 and μ2 are the population means of the two groups.
T137 20484-20715 Sentence denotes From Table 2, we can observe that the t-statistic (5.91), which when compared with critical t value (1.67) at 5% level of significance (α), rejected the null hypothesis and confirmed the significant reduction in the AQI for site 1.
T138 20716-20829 Sentence denotes The p value was also found to be very small, suggesting that the COVID-19 pandemic confinement reduced AQI (45%).
T139 20830-20979 Sentence denotes The p value revealed it is “unlikely” that we would observe such an extreme test statistic t* in the direction of HA if the null hypothesis was true.
T140 20980-21065 Sentence denotes Therefore, the initial assumption that the null hypothesis is true must be incorrect.
T141 21066-21229 Sentence denotes That is, since the p value, 0.00000015, is very less than α = 0.05, we reject the null hypothesis H0 : μ1 = μ2 in favor of the alternative hypothesis HA : μ1 > μ2.
T142 21230-21380 Sentence denotes However, if we lowered our willingness to make a type I error to α = 0.01 instead, the significant rejection of the null hypothesis is again observed.
T143 21381-21517 Sentence denotes This is due to reduction in anthropogenic activities including fuel and coal burning, vehicular emissions, and manufacturing industries.
T144 21518-21560 Sentence denotes Table 2 Welch’s two-sample t test analysis
T145 21561-21588 Sentence denotes Site 1 Site 2 Site 3 Site 4
T146 21589-21660 Sentence denotes Sample A Sample B Sample A Sample B Sample A Sample B Sample A Sample B
T147 21661-21717 Sentence denotes Mean 241.65 134.25 159.12 65.77 144.86 57.45 75.78 63.20
T148 21718-21754 Sentence denotes Observations 36 35 36 35 36 35 36 35
T149 21755-21791 Sentence denotes Hypothesized mean difference 0 0 0 0
T150 21792-21821 Sentence denotes Degree of freedom 62 38 40 42
T151 21822-21907 Sentence denotes 95% confidence interval (71.05, 143.75) (69.45, 116.99) (63.14, 111.65) (1.09, 24.05)
T152 21908-21939 Sentence denotes t-statistic 5.91 7.94 7.28 2.20
T153 21940-21999 Sentence denotes P (T ≤t) one-tail 0.00000015 0.0000000014 0.0000000074 0.03
T154 22000-22039 Sentence denotes t Critical one-tail 1.67 1.68 1.68 1.68
T155 22040-22317 Sentence denotes The same behavior can be observed from the data of Table 2 for 2nd, 3rd, and 4th studied locations where the much lowered p values exhibited the statistically significant effect of COVID-19 pandemic confinement in lowering the sample mean AQI by 58%, 60%, and 17% respectively.
T156 22319-22329 Sentence denotes Conclusion
T157 22330-22595 Sentence denotes The present study demonstrates the impact of COVID-19 pandemic confinement on air quality among the populous site of four major metropolitan cities in India i.e., site 1—ITO, Delhi, site 2—Worli, Mumbai, site 3—Jadavpur, Kolkata, and site 4—Manali Village, Chennai.
T158 22596-22759 Sentence denotes A data set was constructed for AQI, PM2.5, PM10, NO2, NH3, SO2, CO, and ozone from January 1, 2020 to May 31, 2020 from the Central Pollution Control Board (CPCB).
T159 22760-22937 Sentence denotes Pearson’s correlation analysis and Welch’s t test were performed for the determination of statistically significant improvement in the air quality during the confinement period.
T160 22938-23087 Sentence denotes A remarkable drop falls of 44%, 59%, 59%, and 6% in AQI which was observed during COVID-19 pandemic confinement in sites 1, 2, 3, and 4 respectively.
T161 23088-23302 Sentence denotes It can be concluded that remarkable improvement in the air quality during confinement period was observed as the p values of the test for all of the four sites were very less than the significance level (α = 0.05).
T162 23303-23491 Sentence denotes Besides, the Welch’s t test was supported by findings of Pearson’s correlation analysis in which the prominent pollutants (PM2.5 and PM10) were also found to be highly correlated with AQI.
T163 23492-23693 Sentence denotes Although a significant impact on planetary health can be noticed during COVID-19 pandemic confinement, the circumstance is momentary and limits for a short duration i.e., only up to confinement period.