Ambient Particulate Matters (PM 10 , PM 2.5 and PM 0.1 ) were investigated at Shinjung station in New Taipei City, Taiwan. Samples were collected simultaneously using a dichotomous sampler (Andersen Model SA-241) and a MOUDI (MSP Model 110) over a 24-h period from May 2011 to November 2011 at Shinjung station. Samples were analyzed for metallic trace elements using ion coupled plasma mass spectroscopy (ICP-MS) and ionic compounds by ion chromatography (IC). The average concentrations of PM 10 , PM 2.5 and PM 0.1 were found to be 39.45 ± 11.58, 21.82 ± 7.50 and 1.42 ± 0.56 μg/m 3 , respectively. Based on the chemical information, positive matrix factorization (PMF) was used to identify PM sources. A total of five source types were identified, including soil dust, vehicle emissions, sea salt, industrial emissions and secondary aerosols, and their contributions were estimated using PMF. The crustal enrichment factors (EF) were calculated using Al as a reference for the trace metal species to identify the sources. Conditional probability functions (CPF) were computed using wind profiles and factor contributions. The results of CPF analysis were used to identify local point sources. The results suggest a competitive relationship between anthropogenic and natural source processes over the monitoring station.

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The World Health Organization (WHO) declared the coronavirus disease of 2019 (COVID-19) as a pandemic due to its widespread global infection. This has resulted in lockdown under different phases in many nations, including India, around the globe. In the present study, we report the impact of aerosols on surface ozone in the context of pre-lockdown (01 st - 24th March 2020 (PLD)), lockdown phase1 (25th March to 14th April 2020 (LDP1)), and lockdown phase 2 (15th April to 03 rd May 2020 (LDP2)) on clear days at a semi-arid site, Anantapur in southern India using both in situ observations and model simulations. Collocated measurements of surface ozone (O 3 ), aerosol optical depth (AOD), black carbon mass concentration (BC), total columnar ozone (TCO), solar radiation (SR), and ultraviolet radiation (UV-A) data were collected using an Ozone analyzer, MICROTOPS sunphotometer, Ozonometer, Aethalometer, and net radiometer during the study period. The diurnal variations of O 3 and BC exhibited an opposite trend during three phases. The concentrations of ozone were ∼10.7% higher during LDP1 (44.8 ± 5.2 ppbv) than the PLD (40.5 ± 6.0 ppbv), which mainly due to an unprecedented reduction in NOx emissions leading to a lower O 3 titration by NO. The prominent increase in the surface zone during LDP1 is reasonably consistent with the observed photolysis frequencies (j (O 1 D)) through Tropospheric Ultraviolet and Visible (TUV) model. The results show that a pronounced spectral and temporal variability in the AOD during three lockdown phases is mainly due to distinct aerosol sources. The increase in AOD during LDP2 due to long-range transport can bring large amounts of mineral dust and smoke aerosols from the west Asian region and central India, and which is reasonably consistent with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) air mass back trajectories and Moderate Resolution Imaging Spectroradiometer (MODIS) fire counts analysis over the measurement location. Overall, a drastic reduction in BC concentration (∼8.4%) and AOD (10.8%) were observed in the semi-arid area during LDP1 with correspondence to PLD. The columnar aerosol size distributions retrieved from the spectral AODs followed power-law plus unimodal during three phases. The absorption angstrom exponent (AAE) analysis reveals a predominant contribution to the BC from biomass burning activities during the lockdown period over the measurement location.

The novel coronavirus disease is known as COVID-19, which is declared as a pandemic by the World Health Organization during March 2020. In this study, the COVID-19 connection with various weather parameters like temperature, wind speed, and relative humidity is investigated and the future scenario of COVID-19 is predicted based on the Gaussian model (GM). This study is conducted in Delhi, the capital city of India, during the lowest mobility rate due to strict lockdown nationwide for about two months from March 15 to May 17, 2020. Spearman correlation is applied to obtain the interconnection of COVID-19 cases with weather parameters. Based on statistical analysis, this has been observed that the temperature parameter shows a significant positive trend during the period of study. The number of confirmed cases of COVID-19 is fitted with respect to the number of days by using the Gaussian curve and it is estimated on the basis of the model that maximum cases will go up to 123,886 in number. The maximum number of cases will be observed during the range of 166 ± 36 days. It is also estimated by using the width of the fitted GM that it will take minimum of 10 months for the complete recovery from COVID-19. Additionally, the linear regression technique is used to find the trend of COVID-19 cases with temperature and it is estimated that with an increase in temperature by 1 °C, 30 new COVID-19 cases on daily basis will be expected to observe. This study is believed to be a preliminary study and to better understand the concrete relationship of coronavirus, at least one complete cycle is essential to investigate. The laboratory-based study is essential to be done to support the present field-based study. Henceforth, based on preliminary studies, significant inputs are put forth to the research community and government to formulate thoughtful strategies like medical facilities such as ventilators, beds, testing centers, quarantine centers, etc., to curb the effects of COVID-19. Electronic supplementary material The online version of this article (10.1007/s10668-020-01000-9) contains supplementary material, which is available to authorized users.

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