Source apportionment of gaseous Nitrophenols and their contribution to HONO formation in an urban area.

Nitrophenols (NPs) have significant impacts on human health, climate, and atmospheric chemistry. Despite numerous measurements of particulate NPs, still little is known about their gaseous atmospheric abundances, sources, and fate. Here, four gaseous NPs [2,4-dinitrophenol (2,4-DNP), 4-nitrophenol (4-NP), 2-nitrophenol (2-NP), and 2-Methyl-4-nitrophenol (2-Me-4-NP)] were continuously monitored during late Spring at an urban site in Houston, Texas. Among the four NPs, 4-NP showed the highest abundance, followed by 2-Me-4-NP, 2-NP, and 2,4-DNP with average concentrations of 1.07 ± 0.19 ppt, 0.47 ± 0.12 ppt, 0.41 ± 0.16 ppt, and 0.27 ± 0.09 ppt, respectively. The positive matrix factorization (PMF) model identified seven sources: industrial NPs, secondary formation, phenol sources, acetonitrile source, natural gas/crude oil, traffic, and petrochemical industries/oil refineries. A zero-dimensional photochemical box model was used to simulate the observed 2-NP and 2,4-DNP. A 50.0% and 70.0% jNO2 was found to be consistent with the measured 2-NP and 2,4-DNP. This yields a nitrous acid (HONO) production of 7.5 ± 2.5 ppt/h from 06:00 to 18:00 Central Standard Time (CST) from both NPs. An extrapolation including other known NPs suggests a maximum HONO formation of 13.8 ppt/h. The results of this study suggest that using PMF analysis supplemented by photochemical box model provides identification of the NPs sources and their atmospheric implication to HONO formation.

SARS-CoV-2 detection in hospital indoor environments, NW Iran.

This study aimed to investigate the potential contamination of SARS-CoV-2 in indoor settled dust and surfaces of Amir Al-Muminin hospital in Maragheh, Iran. Samples were taken from surfaces and settled dust using a passive approach and particulate matter (PM) using an active approach from different hospital wards. SARS-CoV-2 was detected in 15% of settled dust samples (N = 4/26) and 10% of surface samples (3/30). SARS-CoV-2 has been detected in 13.8% and 9.1% of the dust samples collected at a distance of fewer than 1 m and more than 3 m from the patient bed, respectively. SARS-CoV-2 was found in 11% of surface samples from low-touch surfaces and 8% from high touch surfaces. The relationship between PM2.5, PM10, humidity, temperature, and positive samples of SARS-CoV-2 was investigated. A positive correlation was observed between relative humidity, PM2.5, and positive SARS-CoV-2 samples. Principal component analysis (PCA) suggested positive correlation between positive SARS-CoV-2 samples, relative humidity, and PM2.5. Risk assessment results indicated that the annual mean infection risk of SARS-CoV-2 for hospital staff with illness and death was 2.6 × 10-2 and 7.7 × 10-4 per person per year. Current findings will help reduce the permanence of viral particles in the COVID 19 tragedy and future similar pandemics e.g., novel influenza viruses.

Particulate matters and gaseous pollutants in indoor environment and Association of ultra-fine particulate matters (PM1) with lung function.

Real-time particulate matters (PM1, PM2.5, PM4, PM7, PM10, and TSP) with AEROCET 531S (USA), gaseous pollutants (NO2 and TVOC) with Aeroquel 500 gas sampler (NZ) were measured from the indoor air of houses at four residential locations in Dhaka, Bangladesh. PM10 samples were collected on quartz filters with a dual channel dust sampler (IPM-FDS 2510, India) for selected trace metal determination from five houses of Dhaka. Respiratory function of the occupants was assessed by using a peak expiratory flow meter (Rossmax PF 120). Mean PM1, PM2.5, and PM10 concentrations were 46.1 ± 13.4, 76.0 ± 16.2, and 203.9 ± 44.8 µg m-3, respectively. Higher enrichment factors of Pb, Zn, and Ni were found for traffic, industrial, and constructional activities. The correlation between indoor and outdoor PM2.5 (R2 = 0.42) and ratios (I/O < 1) suggesting indoor air was effected by outdoor air. The concentration of NO2 (0.076 ± 0.007 ppm) and TVOC (90.0 ± 46.0 ppm) was found above than other studies. The average total hazard ratio (THR) in Dhaka was 9.06 and has the highest exposure to air pollutants (PM2.5, PM10, NO2) in Khilkhet (THR 10.1) residents. A negative association between ultra-fine particles (PM1) and peak flow rate measurements of the residents living in these houses indicates that inhalations of ultra-fine particles has great influence on the reduced lung efficiency.