scholarly journals Assimilative capacity of the atmosphere at Lucknow with respect to air pollution

MAUSAM ◽  
2021 ◽  
Vol 50 (3) ◽  
pp. 263-268
Author(s):  
P .K. NANDANKAR
Keyword(s):  
Air Pollution ◽  
Meteorological Data ◽  
Vertical Mixing ◽  
Uttar Pradesh ◽  
Southwest Monsoon ◽  
Pollution Potential ◽  
Mixing Height ◽  
Assimilative Capacity ◽  
Diffusion Capacity ◽  
Ventilation Coefficient

The present study aim at seasonal and diurnal pollution potential at Lucknow, the capital of Uttar Pradesh. To assess the pollution potential, meteorological data for five year period (1982-86) of Lucknow have been analyzed for four season, viz.; Winter (December-February), Summer (March-May), Southwest Monsoon (June-September) and Post Monsoon (October-November). Seasonwise wind roses, stability, stability wind roses have been prepared and season wise diurnal variation of mixing height and ventilation coefficient have also been worked out. It is found that Lucknow has a better diffusion capacity in summer and poor in winter. Afternoon hours are better for vertical mixing. The winds are predominant from west to north direction in all season except in monsoon where it blows from east direction.

scholarly journals Assimilative capacity of the atmosphere at Gorakhpur with respect to air pollution

MAUSAM ◽  
2022 ◽  
Vol 53 (4) ◽  
pp. 481-486
Author(s):  
P. K. NANDANKAR
Keyword(s):  
Meteorological Data ◽  
Vertical Mixing ◽  
Uttar Pradesh ◽  
Pollution Potential ◽  
Mixing Height ◽  
Assimilative Capacity ◽  
Diffusion Capacity ◽  
Post Monsoon ◽  
Four Seasons ◽  
Ventilation Coefficient

The present study aims at seasonal and diurnal pollution potential at Gorakhpur in east Uttar Pradesh. To assess the pollution potential, meteorological data for five year period (1982-86) of Gorakhpur have been analyzed for four seasons viz; winter (December-February), summer (March-May), monsoon (June-September) and post monsoon (October-November). Season wise wind roses, stability, stability wind roses have been prepared and season wise diurnal variation of mixing height and ventilation coefficient have also been worked out. It is found that Gorakhpur has a better diffusion capacity in summer and poor in post monsoon followed by winter. Afternoon hours are better for vertical mixing. The winds are predominantly from southwest to west in all seasons except in monsoon when it blows from northeast to east. Based on this study, an appropriate location for industrialization has been suggested.

scholarly journals Dispersion climatology for Patna and Gaya

MAUSAM ◽  
2022 ◽  
Vol 44 (2) ◽  
pp. 199-204
Author(s):  
BIJENDRA RAI
Keyword(s):  
Meteorological Data ◽  
Pollution Potential ◽  
Mixing Height ◽  
Unstable Condition ◽  
Vertical Diffusion ◽  
Night Time ◽  
Stable Conditions ◽  
Set Up ◽  
Ventilation Coefficient ◽  
The City

The present study aims at seasonal and diurnal pollution potential around Patna, the capital region of Bihar and Gaya. To assess the pollution potential, meteorological data of two stations, VIZ., Patna and the neighbouring station Gaya for five year period (1984-88) have been analysed; The analysis has been done for four representative seasonal months, viz., winter (January), pre-monsoon (April), monsoon (August) and post-monsoon (October).   The analysis shows no stable conditions in the day time and no unstable condition in the  night time in each month. April shows higher frequency and January the lowest frequencies of unstable conditions. April  has the highest mixing height and ventilation coefficient. From the results it has been concluded that day time is suitable for good dispersion in all the months. In the ca5e of existing industries, emission must be lessened during night time and particularly in the winter months. These results also suggest that pollutants are well dispersed in April and August. January and August may be regarded as the worst months for vertical diffusion of contaminants. As the predominant surface winds are easterly, any new Industrial set up should be in the west of the city in order to minimise the effects of pollutants.  

Local Weather and Air Pollution Potential: The Case of Durban

1980 ◽  
Vol 7 (3) ◽  
pp. 241-244 ◽  
Author(s):  
Robert A. Preston-Whyte ◽  
Roseanne D. Diab
Keyword(s):  
Air Pollution ◽  
Vertical Mixing ◽  
Decision Makers ◽  
Lower Atmosphere ◽  
Daily Maximum ◽  
Pollution Potential ◽  
Mixing Depth ◽  
Horizontal Transport ◽  
Local Weather ◽  
Meso Scale

Atmospheric pollution over cities accumulates under light wind or stagnation conditions and, on occasion, may be supplemented by transport from distant sources. These conditions cannot easily be predicted by use of the average weather elements. However, material which is useful to decision-makers who are concerned with air pollution problems can be obtained by presenting the data, as in the case of Durban, South Africa, first in terms of the nature and characteristics of vertical mixing in the lower atmosphere, and secondly in terms of the horizontal transport of air. In this way the nature and characteristics of surface and non-surface inversions and mixingdepths, as well as of macro- and meso-scale atmospheric circulations, can more easily be appreciated. In addition, a measure of the air pollution potential can be obtained from daily maximum mixing-depth and win-speed values.

STATISTICAL METHODS FOR THE ASSESSMENT OF AIR POLLUTION OF JHANSI CITY, UTTAR PRADESH, INDIA

2018 ◽  
Vol 24 (1) ◽  
Author(s):  
V. S. CHAUHAN ◽  
BHANUMATI SINGH ◽  
SHREE GANESH ◽  
JAMSHED ZAIDI
Keyword(s):  
Air Pollution ◽  
Uttar Pradesh ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Base Line ◽  
Multivariate Statistical ◽  
Large Cities ◽  
Governmental Organizations ◽  
Non Governmental Organizations ◽  
The Status

Studies on air pollution in large cities of India showed that ambient air pollution concentrations are at such levels where serious health effects are possible. This paper presents overview on the status of air quality index (AQI) of Jhansi city by using multivariate statistical techniques. This base line data can help governmental and non-governmental organizations for the management of air pollution.

scholarly journals The modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rui Zhang ◽  
Yujie Meng ◽  
Hejia Song ◽  
Ran Niu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Air Pollution ◽  
High Temperature ◽  
Air Pollutants ◽  
Meteorological Data ◽  
Effect Of Temperature ◽  
Distributed Lag ◽  
Modification Effect ◽  
Incidence Risk ◽  
The Impact ◽  
The Relationship

Abstract Background Although exposure to air pollution has been linked to many health issues, few studies have quantified the modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China. Methods The data of daily incidence of influenza and the relevant meteorological data and air pollution data in Ningbo from 2014 to 2017 were retrieved. Low, medium and high temperature layers were stratified by the daily mean temperature with 25th and 75th percentiles. The potential modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo was investigated through analyzing the effects of air pollutants stratified by temperature stratum using distributed lag non-linear model (DLNM). Stratified analysis by sex and age were also conducted. Results Overall, a 10 μg/m3 increment of O3, PM2.5, PM10 and NO2 could increase the incidence risk of influenza with the cumulative relative risk of 1.028 (95% CI 1.007, 1.050), 1.061 (95% CI 1.004, 1.122), 1.043 (95% CI 1.003, 1.085), and 1.118 (95% CI 1.028, 1.216), respectively. Male and aged 7–17 years were more sensitive to air pollutants. Through the temperature stratification analysis, we found that temperature could modify the impacts of air pollution on daily incidence of influenza with high temperature exacerbating the impact of air pollutants. At high temperature layer, male and the groups aged 0–6 years and 18–64 years were more sensitive to air pollution. Conclusion Temperature modified the relationship between air pollution and daily incidence of influenza and high temperature would exacerbate the effects of air pollutants in Ningbo.

Renewable energy and energy storage to offset diesel generators at expeditionary contingency bases

2021 ◽  
pp. 154851292110513
Author(s):  
Scott M Katalenich ◽  
Mark Z Jacobson
Keyword(s):  
Air Pollution ◽  
Renewable Energy ◽  
Energy Storage ◽  
Carbon Emissions ◽  
Meteorological Data ◽  
Vertical Axis ◽  
Humanitarian Assistance ◽  
Diesel Generators ◽  
Combat Casualties ◽  
The Military

Expeditionary contingency bases (non-permanent, rapidly built, and often remote outposts) for military and non-military applications represent a unique opportunity for renewable energy. Conventional applications rely upon diesel generators to provide electricity. However, the potential exists for renewable energy, improved efficiency, and energy storage to largely offset the diesel consumed by generators. This paper introduces a new methodology for planners to incorporate meteorological data for any location worldwide into a planning tool in order to minimize air pollution and carbon emissions while simultaneously improving the energy security and energy resilience of contingency bases. Benefits of the model apply not just to the military, but also to any organization building an expeditionary base—whether for humanitarian assistance, disaster relief, scientific research, or remote community development. Modeling results demonstrate that contingency bases using energy efficient buildings with batteries, rooftop solar photovoltaics, and vertical axis wind turbines can decrease annual generator diesel consumption by upward of 75% in all major climate zones worldwide, while simultaneously reducing air pollution, carbon emissions, and the risk of combat casualties from resupply missions.

scholarly journals Experimental and artificial neural network approach for forecasting of traffic air pollution in urban areas: The case of Subotica

2010 ◽  
Vol 14 (suppl.) ◽  
pp. 79-87 ◽  
Author(s):  
Bogdana Vujic ◽  
Srdjan Vukmirovic ◽  
Goran Vujic ◽  
Nebojsa Jovicic ◽  
Gordana Jovicic ◽  
...  
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Meteorological Data ◽  
Model Development ◽  
Ambient Air ◽  
Ann Model ◽  
Statistical Parameters ◽  
Neural Network Approach ◽  
Starting Point ◽  
Artificial Neural

In the recent years, artificial neural networks (ANNs) have been used to predict the concentrations of various gaseous pollutants in ambient air, mainly to forecast mean daily particle concentrations. The data on traffic air pollution, irrespective of whether they are obtained by measuring or modelling, represent an important starting point for planning effective measures to improve air quality in urban areas. The aim of this study was to develop a mathematical model for predicting daily concentrations of air pollution caused by the traffic in urban areas. For the model development, experimental data have been collected for 10 months, covering all four seasons. The data about hourly concentration levels of suspended particles with aerodynamic diameter less than 10 ?m (PM10) and meteorological data (temperature, air humidity, speed and direction of wind), measured at the measuring station in the town of Subotica from June 2008 to March 2009, served as the basis for developing an ANN-based model for forecasting mean daily concentrations of PM10. The quality of the ANN model was assessed on the basis of the statistical parameters, such as RMSE, MAE, MAPE, and r.

scholarly journals PREDICTING DAILY PM2.5 USING WEIGHTED LONG SHORT-TERM MEMORY NEURAL NETWORK MODEL

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1451-1455
Author(s):  
H. Fan ◽  
M. Yang ◽  
F. Xiao ◽  
K. Zhao
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Series Data ◽  
Wind Condition ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Pm2.5 Concentration

Abstract. Over the past few decades, air pollution has caused serious damage on public health, thus making accurate predictions of PM2.5 crucial. Due to the transportation of air pollutants among areas, the PM2.5 concentration is strongly spatiotemporal correlated. However, the distribution of air pollution monitoring sites is not even, making the spatiotemporal correlation between the central site and surrounding sites varies with different density of sites, and this was neglected by most existing methods. To tackle this problem, this study proposed a weighted long short-term memory neural network extended model (WLSTME), which addressed the issue that how to consider the effect of the density of sites and wind condition on the spatiotemporal correlation of air pollution concentration. First, several the nearest surrounding sites were chosen as the neighbour sites to the central station, and their distance as well as their air pollution concentration and wind condition were input to multi-layer perception (MLP) to generate weighted historical PM2.5 time series data. Second, historical PM2.5 concentration of the central site and weighted PM2.5 series data of neighbour sites were input into LSTM to address spatiotemporal dependency simultaneously and extract spatiotemporal features. Finally, another MLP was utilized to integrate spatiotemporal features extracted above with the meteorological data of central site to generate the forecasts future PM_2.5 concentration of the central site. Daily PM_2.5 concentration and meteorological data on Beijing–Tianjin–Hebei from 2015 to 2017 were collected to train models and evaluate the performance. Experimental results with 3 other methods showed that the proposed WLSTME model has the lowest RMSE (40.67) and MAE (26.10) and the highest p (0.59). This finding confirms that WLSTME can significantly improve the PM2.5 prediction accuracy.

scholarly journals A First Review to Explore the Association of Air Pollution (PM and NO2) on Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2)

2020 ◽  
Author(s):  
Chiara Copat ◽  
Antonio Cristaldi ◽  
Maria Fiore ◽  
Alfina Grasso ◽  
Pietro Zuccarello ◽  
...  
Keyword(s):  
Air Pollution ◽  
Meteorological Data ◽  
Scientific Evidence ◽  
Epidemiological Studies ◽  
Negative Influence ◽  
Health Concern ◽  
Mortality Data ◽  
Great Effort ◽  
Incidence Data ◽  
Long Time

A new coronavirus (SARS-CoV-2) have determined a pneumonia outbreak in China (Wuhan and Hubei) on December 2019. While pharmaceutical and non-pharmaceutical intervention strategies are strengthened worldwide, the scientific community has been studying the risk factors associated with SARS-Cov-2, to enrich epidemiological information. For a long time, before the industrialized era, air pollution has been a real and big health concern and it is today a very serious environmental risk for many diseases and anticipated deaths in the world. It has long been known that air pollutants increasing the invasiveness of pathogens for humans by acting as a carrier and making people more sensitive to pathogens through a negative influence on the immune system. Based on scientific evidences, the hypothesis that air pollution, resulting from a combination of factors such as meteorological data, level of industrialization as well as regional topography, can acts both as an infection carrier as a harmful factor of the health outcomes of COVID-19 disease has been raised recently. This hypothesis is turning in scientific evidence, thanks to the numerous studies that have been launched all over the world.With this review, we want to provide a first unique view of all the first epidemiological studies relating the association between air pollution and SARS-CoV-2. The Authors, who first investigated this association, although with great effort and rapidity of analysis dictated by a global emergency, often used different research methods or not all include confounding factors whenever possible. In addition, to date incidence data are underestimated in all countries, and to a lesser extent also mortality data. For this reason, the cases included in the considered studies cannot be considered real. Although it determines important limitations for direct comparison of results, and more studies are needed to strengthen scientific evidences and support firm conclusions, major findings are consistent, highlighting the important contribution of PM2.5 and NO2 on the COVID-19 spread and with a less extent also PM10.

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