scholarly journals Precipitation chemistry over the Indian region

MAUSAM ◽  
2021 ◽  
Vol 43 (3) ◽  
pp. 249-258
Author(s):  
B. MUKHOPADHYAY ◽  
S.V. DATAR ◽  
H.N. SRIVASTAVA
Keyword(s):  
Electrical Conductivity ◽  
Monitoring Network ◽  
Precipitation Chemistry ◽  
Indian Region ◽  
Pollution Monitoring ◽  
Steady Increase ◽  
Alkaline Soil ◽  
Marine Aerosols ◽  
Air Pollution Monitoring ◽  
Background Air

The present study is based on the precipitation chemistry data from the Background Air Pollution Monitoring Network (BAPMoN) in the Indian region, for the period 1976-87. Sampling is made on an event basis and the pH and electrical conductivity of the samples are determined from filtered samples immediately after cessation of rain. The chemical analysis is performed on monthly mixed samples.   No trend is found in the pH of rainwater from background areas except at Allahabad, Pune and Visakhapatnam which suffer from sizable anthropogenic influences. The pH seems to be related more to NO3 ions compared to SO4-2 ions. A natural buffer appearing in the form of alkaline soil-derived species seems adequateat most places (except Mohanbari), in keeping a check on progressive acidification despite steady increase in concentration of nitrates. The ion balance cannot be achieved without including the presence of HCO3, which when done explains the observed electrical conductivity of rainwater. The interaction of marine aerosols with acid aerosols has also been studied for the marine regions in the Indian areas and reveals a substantial removal of chloride from sea-salt. Inland sources of NaCl have also been identified from the BAPMoN data.

scholarly journals H2020 project CAPTOR dataset: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network.

Data in Brief ◽  
2021 ◽  
pp. 107127
Author(s):  
Jose M. Barcelo-Ordinas ◽  
Pau Ferrer-Cid ◽  
Jorge Garcia-Vidal ◽  
Mar Viana ◽  
Ana Ripoll
Keyword(s):  
Air Pollution ◽  
Low Cost ◽  
Monitoring Network ◽  
Pollution Monitoring ◽  
Raw Data ◽  
Air Pollution Monitoring

The Global Environment Monitoring System (GEMS) of UNEP

1982 ◽  
Vol 9 (1) ◽  
pp. 35-41 ◽  
Author(s):  
Michael D. Gwynne
Keyword(s):  
United Nations ◽  
Monitoring System ◽  
Monitoring Network ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
The World ◽  
The United Nations ◽  
United Nations System ◽  
Activity Centre ◽  
Nations System

The Global Environment Monitoring System (GEMS) is a collective effort of the world community to acquire, through monitoring, the data needed for rational management of the environment, and arose from recommendations of the United Nations Conference on the Human Environment which was held in Stockholm in 1972. The GEMS Programme Activity Centre (PAC) at UNEP headquarters in Nairobi, Kenya, coordinates all that it can of the various environmental monitoring activities which are carried on throughout the world—particularly those within the United Nations System.Great care is taken to ensure that data gathered by GEMS are of the highest attainable quality, and that data collected from different parts of a particular monitoring network are both comparable and compatible. The GEMS Programme Activity Centre (PAC), in the manner of UNEP itself, is not operational but works mainly through the intermediary of the Specialized Agencies of the United Nations System—most notably FAO, ILO, UNESCO, WHO, and WMO—together with appropriate intergovernmental organizations such as IUCN.The GEMS monitoring system consists of five closelyinterrelated programmes which have built-in provision for training and for rendering technical assistance to ensure the participation of countries that are inadequately provided with personnel and equipment. The five are:1. Climate-related monitoring;2. Monitoring of long-range transport of pollutants;3. Health-related monitoring (concerned with pollutional effects);4. Ocean monitoring; and5. Terrestrial renewable-resource monitoring.Each of these broad areas contains at least five distinct world-wide monitoring networks. Examples of these latter are the World Glacier Inventory, Background Air Pollution Monitoring Network, Urban Air Pollution Monitoring Network, Global Water Quality Monitoring Network, Tropical Forest Monitoring Network, Species Conservation Monitoring Network, etc.Monitored data are gathered at suitable coordinating centres for each network at which appropriate data-bases have been, or are being, established. Data are analyzed to produce periodic regional and global assessments which are reported at intervals that are appropriate to the variable which is being considered.

Urgent problems of improving background air pollution monitoring systems

1988 ◽  
Vol 11 (3) ◽  
pp. 269-278
Author(s):  
M. E. Berlyand ◽  
N. Sh. Volberg ◽  
R. F. Lavrinenko ◽  
E. N. Rusina
Keyword(s):  
Air Pollution ◽  
Pollution Monitoring ◽  
Monitoring Systems ◽  
Air Pollution Monitoring ◽  
Background Air

scholarly journals Treands in atmospheric turbidity over India

MAUSAM ◽  
2021 ◽  
Vol 43 (2) ◽  
pp. 183-190
Author(s):  
H. N. SRIVASTAVA ◽  
S. V. DATAR ◽  
B. MUKHOPADHYAY
Keyword(s):  
Monitoring Network ◽  
Volcanic Eruptions ◽  
Pollution Monitoring ◽  
Anthropogenic Sources ◽  
Data Set ◽  
Mean Values ◽  
Air Pollution Monitoring ◽  
Sources Of Pollution ◽  
Atmospheric Turbidity ◽  
Aerosol Dispersion

Annual mean values of the turbidity coefficients at Indian Background Air Pollution Monitoring Network' (BAPMoN) were compared for the periods 1973-1980and 1981-1985. It was found that there is a general increase of turbidity during the latter period at all the stations except at Kodaikanal and Pune, suggesting the effect of anthropogenic sources of pollution. Short term influence of volcanic eruptions were also discernible from the observations at Kodaikanal. Spectral analysis (FFT) at these stations brought out the predominant modes which could be explained on the basis of climatology and aerosol dispersion characteristics. The long term atmospheric turbidity observations (1973-1985) presented in this paper provide reliable data set for assessing the aerosol impact on radiation climate.  

scholarly journals Evaluation of the LOTOS-EUROS NO<sub>2</sub> simulations using ground-based measurements and S5P/TROPOMI observations over Greece

2020 ◽  
Author(s):  
Ioanna Skoulidou ◽  
Maria-Elissavet Koukouli ◽  
Astrid Manders ◽  
Arjo Segers ◽  
Dimitris Karagkiozidis ◽  
...  
Keyword(s):  
Spatial Correlation ◽  
Extreme Values ◽  
Transport Model ◽  
Monitoring Network ◽  
Temporal Correlation ◽  
Chemical Transport ◽  
Pollution Monitoring ◽  
Chemical Transport Model ◽  
Air Pollution Monitoring ◽  
Tropospheric No2

Abstract. The evaluation of chemical transport models, CTMs, is essential for the assessment of their performance regarding the physical and chemical parameterizations used. While regional CTMs have been widely used and evaluated over Europe, their validation over Greece is limited. In this study, we investigate the performance of the LOTOS-EUROS v2.2.001 regional chemical transport model in simulating nitrogen dioxide, NO2, over Greece from June to December 2018. In-situ NO2 measurements obtained from the National Air Pollution Monitoring Network are compared with surface simulations over the two major cities of Greece, Athens and Thessaloniki. The model reproduces well the spatial variability of the measured NO2 with a spatial correlation coefficient of 0.85 for the period between June and December 2018. About half of the 14 air quality monitoring stations show a good temporal correlation to the simulations, higher than 0.6, during daytime (12–15 p.m. local time), while the corresponding biases are negative. Most stations show stronger negative biases during winter than in summer. Furthermore, the simulated tropospheric NO2 columns are evaluated against ground-based MAX-DOAS NO2 measurements and space-borne Sentinel 5-Precursor TROPOMI tropospheric NO2 observations in July and December 2018. LOTOS-EUROS captures better the NO2 temporal variability in December (0.61 and 0.81) than in July (0.50 and 0.21) when compared to the corresponding measurements of the MAX-DOAS instruments in Thessaloniki and the rural azimuth viewing direction in Athens respectively. The urban azimuth viewing direction in Athens region however shows a better correlation in July than in December (0.41 and 0.19, respectively). LOTOS-EUROS NO2 columns over Athens and Thessaloniki agree well with the TROPOMI observations showing higher spatial correlation in July (0.95 and 0.82, respectively) than in December (0.82 and 0.66, respectively) while the relative temporal correlations are higher during winter. Overall, the comparison of the simulations with the TROPOMI observations shows a model underestimation in summer and an overestimation in winter both in Athens and Thessaloniki. Updated emissions for the simulations and model improvements when extreme values of boundary layer height are encountered are further suggested.

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