The role of particulate matter in reduced visibility and anionic composition of winter fog: a case study for Amritsar city

Winter fog is a complex issue affecting human health and is responsible for higher numbers of traffic accidents in North India, which is further aggravated due to atmospheric pollutants. An indigenous glass-plate fog collector was used to collect fog water from December 2020 to February 2021. Thirty samples of fog water were collected from the rooftop of an academic building at Guru Nanak Dev University, Amritsar, in order to study the chemistry of fog water. The studied parameters were pH, electrical conductivity (EC), sodium (Na+), calcium (Ca2+), magnesium (Mg2+), potassium (K+) sulphate (SO42−), nitrate (NO3−), chloride (Cl−), aluminum (Al), lead (Pb) and zinc (Zn). The average values were as follows: pH (4.6–7.5), EC (135 µS/cm), SO42− (77.5 ppm), Cl− (9.9 ppm), NO3− (9.3 ppm), Ca2+ (8.1 ppm), Mg2+ (2.0 ppm), K+ (2.0 ppm), Na+ (1.6 ppm), Zn (218 ppb), Al (60.8 ppb) and Pb (8.8 ppb). Cation–anion balance was used to assess the data’s reliability. The enrichment factor (EF) was utilized to distinguish between crustal and anthropogenic sources. SO42−, NO3−, Cl− and K+ originated from anthropogenic sources, whereas Mg2+ and Na+ came from crustal sources. The molar ratio of sulphate to nitrate was 10.6, which indicates a greater contribution from the combustion of fossil fuels and stack emissions. Ionic species were subjected to principal component analysis (PCA) as a dimensionality reduction approach and to group species with comparable behavior. Three principal components (PC) that together accounted for 77.5 percent of the total variance were identified by PCA. Backward trajectory analysis of air masses was performed to determine their origin, and two major clusters explained 89 percent of the contribution of air masses, primarily from the north-east and north directions. To gain a comprehensive understanding of fog water, a global perspective on pH, EC and ionic species is considered.

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Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM_2.5 and visibility predictions

Li¹,

Wang²,

Wang³

et al. 2023

Geosci. Model Dev.

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Abstract. Data assimilation techniques are one of the most important ways to reduce the uncertainty in atmospheric chemistry model input and improve the model forecast accuracy. In this paper, an ensemble optimal interpolation assimilation (EnOI) system for a regional online chemical weather numerical forecasting system (GRAPES_Meso5.1/CUACE) is developed for operational use and efficient updating of the initial fields of chemical components. A heavy haze episode in eastern China was selected, and the key factors affecting EnOI, such as localization length scale, ensemble size, and assimilation moment, were calibrated by sensitivity experiments. The impacts of assimilating ground-based PM2.5 observations on the model chemical initial field PM2.5 and visibility forecasts were investigated. The results show that assimilation of PM2.5 reduces the uncertainty in the initial PM2.5 field considerably. Using only 50 % of observations in the assimilation, the root mean square error (RMSE) of initial PM2.5 for independent verification sites in mainland China decreases from 73.7 to 46.4 µg m−3, and the correlation coefficient increases from 0.58 to 0.84. An even larger improvement appears in northern China. For the forecast fields, assimilation of PM2.5 improves PM2.5 and visibility forecasts throughout the time window of 24 h. The PM2.5 RMSE can be reduced by 10 %–21 % within 24 h, and the assimilation effect is the most remarkable in the first 12 h. Within the same assimilation time, the assimilation efficiency varies with the discrepancy between model forecasts and observations at the moment of assimilation, and the larger the deviation, the higher the efficiency. The assimilation of PM2.5 further contributes to the improvement of the visibility forecast. When the PM2.5 increment is negative, it corresponds to an increase in visibility, and when the PM2.5 analysis increment is positive, visibility decreases. It is worth noting that the improvement of visibility forecasting by assimilating PM2.5 is more obvious in the light-pollution period than in the heavy-pollution period. The results of this study show that EnOI may provide a practical and cost-effective alternative to the ensemble Kalman filter (EnKF) for the applications where computational cost is the main limiting factor, especially for real-time operational forecast.

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The role of particulate matter in reduced visibility and anionic composition of winter fog: a case study for Amritsar city

Abstract: Severe fog events during winter months in India are a serious concern due to the higher incidence of road accidents, flight delays and increased occurrence of respiratory diseases.

Cited by 3 publications

References 47 publications

Comparative analysis of nitrate evolution patterns during pollution episodes: Method development and results from Tianjin, China

Comparative analysis of nitrate evolution patterns during pollution episodes: Method development and results from Tianjin, China

Chemical Composition and Source Apportionment of Winter Fog in Amritsar: An Urban City of North-Western India

Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM_2.5 and visibility predictions

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The role of particulate matter in reduced visibility and anionic composition of winter fog: a case study for Amritsar city

Abstract: Severe fog events during winter months in India are a serious concern due to the higher incidence of road accidents, flight delays and increased occurrence of respiratory diseases.

Cited by 3 publications

References 47 publications

Comparative analysis of nitrate evolution patterns during pollution episodes: Method development and results from Tianjin, China

Comparative analysis of nitrate evolution patterns during pollution episodes: Method development and results from Tianjin, China

Chemical Composition and Source Apportionment of Winter Fog in Amritsar: An Urban City of North-Western India

Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM2.5 and visibility predictions

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Product

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Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM_2.5 and visibility predictions