Table 2.
Prominent studies on environmental concerns of COVID-19.
| Studies | Country | Methods | Model |
|---|---|---|---|
| Studies finding that air quality factors impact covid-19 transmission | |||
| Filippini et al. [43] | Italy | Empirical | Multivariable negative binomial regression model |
| Naqvi et al. [47] | India | Empirical | Correlation |
| Wang et al. [48] | China | Empirical | Generalized additive models |
| Sharma et al. [49] | India | Empirical | |
| Xie and Zhu [50] | China | Empirical | Generalized additive model (GAM) |
| Ong et al. [51] | Singapore | Experimental and Observational | Real-time reverse transcriptase-polymerase chain reaction (RT-PCR) |
| Bashir et al. [52] | California | Empirical | Spearman and Kendall correlation tests |
| Al-Rousan and Al-Najjar [53] | China | Empirical | ARIMA model |
| Zoran et al. [54,55] | Italy | Empirical | |
| Zhu et al. [56] | China | Empirical | Generalized additive model (GAM) |
| Poole [57] | Global | Observational | Deterministic atmospheric weather modeling |
| Prata et al. [58] | Brazil | Empirical | Generalized Additive Model (GAM) |
| Gupta et al. [59] | USA | Empirical | Distribution model |
| Ficetola and Rubolini [60] | Global | Empirical | Linear Mixed Model |
| Kumar [61] | India | Empirical | HYSPLIT (NOAA) forward trajectories model |
| Chin et al. [62] | USA | Experimental | |
| Jahangiri et al. [64] | Iran | Qualitative - Case study | Receiver operating characteristics (ROC) |
| Quilodrán et al. [65] | China | Empirical | Generalized additive model (GAM) |
| Şahin [66] | Turkey | Empirical | |
| Tosepu et al. [67] | Indonesia | Empirical | |
| Ahmadi et al. [68] | Iran | Empirical | Sobol's-Jansen methods & Partial Correlation Coefficient (PCC) |
| Bannister-Meyer et al. [69] | Global | Empirical | A statistical model based on Generalized Linear Regression framework |
| Shi et al. [71] | China | Empirical | SEIR model |
| Wang et al. [72] | Global | Empirical | Generalized linear mixture model |
| Jingyuan Wang et al. [73] | China | Empirical | Fixed/Random effect model |
| Islam et al. [74] | Global | Empirical | Empirical test - Multilevel mixed-effects negative binomial regression models |
| Iqbal et al. [75] | China | Empirical | Wavelet Coherence techniques |
| Pequeno et al. [76] | Brazil | Empirical | Generalized Linear Mixed Model (GLMM) |
| Rosario et al. [77] | Brazil | Empirical | |
| Mandal and Panwar [78] | Global | Empirical | Univariate analysis and statistical modeling |
| Livadiotis [79] | USA and Italian regions | Empirical | |
| Ujiie et al. [80] | Japan | Empirical | Poisson regression analysis |
| Huang et al. [81] | Global | Empirical | |
| Iqbal et al. [82] | Global | Empirical | Coefficient of determination model |
| Méndez-Arriaga [83] | Mexico | Empirical | |
| Pani et al. [84] | Singapore | Empirical | |
| Sharma et al. [85] | Top 10 most infected countries | Empirical | Advanced econometric techniques of panel data regression |
| Shahzad et al. [86] | China | Empirical | Quantile regression model |
| Sobral et al. [87] | Global | Empirical | Panel Data Model |
| Y. Wu et al. [88] | Global | Empirical | Log-linear generalized additive model (GAM) |
| Pirouz et al. [89] | Italy | Empirical | Trend and Multivariate Linear Regression |
| Jain et al. [90] | Afghanistan, India, Pakistan, Bangladesh, Sri Lanka and Nepal | Empirical | Advanced econometric techniques of panel data regression |
| Sharma, Tiwari, et al. [91] | Top 15 most infected countries | Empirical | Wavelet Coherence and Partial Wavelet Coherence |
| Yousefian et al. [5] | Iran | Empirical | |
| Studies observing that lockdown and social distancing impacts the environment positively | |||
| Ju et al. [92] | Korea | Empirical | Paired t-test, sensitivity analysis |
| Hashim et al. [93] | Iraq | Empirical | AQI measurement |
| Chen et al. [94] | China | Empirical | Difference-in-difference approach |
| Wang et al. [95] | China | Empirical | Community Multi-Scale Air Quality (CMAQ) model |
| Xu et al. [96,97] | China | Empirical | |
| Jain and Sharma [98] | India | Empirical | |
| Nadzir et al. [99] | Malaysia | Empirical | Air Sensor network AiRBOXSense |
| Gautam [100] | India | Conceptual | |
| Nakada and Urban [101] | Brazil | Empirical | |
| Lal et al. [102] | Global | Empirical | Coupled Model Inter-comparison Project (CIMIP-5 model) |
| Mahato et al. [103] | India | Empirical | |
| Lokhandwala and Gautam [104] | India | Empirical | |
| Mahato and Ghosh [105] | India | Empirical | MERRA-2 (Modern Era Retrospective-Analysis for Research and Applications, Version 2) |
| Chakraborty and Maity [44] | Global | Observational | |
| Muhammad et al. [106] | China, France, Italy, Spain, USA | Empirical | |
| Myllyvirta and Dahiya [107] | India | Empirical | |
| Paital et al. [108] | India | Observational | |
| Saadat et al. [109] | Global | Conceptual | |
| Wang and Su [110] | China | Empirical | |
| Zambrano-Monserrate et al. [111] | China, USA, Italy, Spain | Conceptual | |
| Dantas et al. [112] | Brazil | Empirical | |
| Thakur et al. [113] | Not Applicable | Conceptual | |
| McGowan [114] | Global | Conceptual | |
| Sarkodie and Owusu [115] | Global | Qualitative and empirical | |
| Atalan [116] | Global | Empirical | Correlation |
| Maithani et al. [117] | India | Empirical | Getis Ord GI* statistic |
| Chakraborty et al. [118] | India | Empirical | WQI, TSI, Pearson's correlation coefficient, and “t” test |
| Studies concluding that long - term exposure to pollutants like NO2 and PM2.5 can be the primary cause of death from COVID-19 | |||
| Ogen [119] | Italy, Spain, France, and Germany | Experimental and Observational | Spatial model |
| Travaglio et al. [120] | England | Empirical | Negative binomial model |
| Wu et al. [121] | USA | Empirical | Zero-inflated negative binomial mixed models |