scholarly journals Visibility over Indian airports during winter season

MAUSAM ◽  
2022 ◽  
Vol 52 (4) ◽  
pp. 717-726
Author(s):  
U. S. DE ◽  
G. S. PRAKASA RAO ◽  
A. K. JASWAL
Keyword(s):  
Boundary Layer ◽  
Air Quality ◽  
Relative Humidity ◽  
Linear Trend ◽  
Winter Season ◽  
Winter Period ◽  
Data Sets ◽  
Level Of Confidence

Visibility plays a key role at the time of landing and take off operations at airports. The daily visibility data from 1969 onwards for 25 stations in the country (at 2100, 0000, 0300 and 0600 UTC) are examined for the winter period. Side by side the dry bulb temperatures and the relative humidity recorded at the same time are also examined. Linear trend regressions have been fitted on the data sets for each of the cities. The significance is tested at 99% level of confidence.   In recent years, degradation of air quality in the cities has often been suggested as the cause for the increase in the number of poor visibility days <2000 meters) particularly in the morning hours. Continuous persistence of this phenomenon for a number of days has also been reported.   The results show that there are decreasing trends in visibility at most of the stations. At 0300 UTC the visibility is generally low and increased afterwards due to mixing and turbulence in the boundary layer.

scholarly journals Vapor isotopic evidence for the worsening of winter air quality by anthropogenic combustion-derived water

2020 ◽  
Vol 117 (52) ◽  
pp. 33005-33010
Author(s):  
Meng Xing ◽  
Weiguo Liu ◽  
Xia Li ◽  
Weijian Zhou ◽  
Qiyuan Wang ◽  
...  
Keyword(s):  
Water Vapor ◽  
Air Quality ◽  
Relative Humidity ◽  
Isotope Composition ◽  
Winter Season ◽  
Energy Structure ◽  
Particulate Pollution ◽  
Guanzhong Basin ◽  
Physical And Chemical

Anthropogenic combustion-derived water (CDW) may accumulate in an airshed due to stagnant air, which may further enhance the formation of secondary aerosols and worsen air quality. Here we collected three-winter-season, hourly resolution, water-vapor stable H and O isotope compositions together with atmospheric physical and chemical data from the city of Xi’an, located in the Guanzhong Basin (GZB) in northwestern China, to elucidate the role of CDW in particulate pollution. Based on our experimentally determined water vapor isotope composition of the CDW for individual and weighted fuels in the basin, we found that CDW constitutes 6.2% of the atmospheric moisture on average and its fraction is positively correlated with [PM2.5] (concentration of particulate matter with an aerodynamic diameter less than 2.5 μm) as well as relative humidity during the periods of rising [PM2.5]. Our modeling results showed that CDW added additional average 4.6 μg m−3 PM2.5 during severely polluted conditions in the GZB, which corresponded to an average 5.1% of local anthropogenic [PM2.5] (average at ∼91.0 μg m−3). Our result is consistent with the proposed positive feedback between the relative humidity and a moisture sensitive air-pollution condition, alerting to the nontrivial role of CDW when considering change of energy structure such as a massive coal-to-gas switch in household heating in winter.

scholarly journals Evaluation of Relationship Between Air Pollutant Concentration and Meteorological Elements in Winter Months

2014 ◽  
Vol 22 (1) ◽  
pp. 25-32 ◽  
Author(s):  
Andrzej Żyromski ◽  
Małgorzata Biniak-Pieróg ◽  
Ewa Burszta-Adamiak ◽  
Zenon Zamiar
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Winter Season ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Meteorological Elements

Abstract The paper presents the evaluation of the relation between meteorological elements and air pollutants’ concentrations. The analysis includes daily concentrations of pollutants and variation of meteorological elements such as wind speed, air temperature and relative humidity, precipitation and total radiation at four monitoring stations located in the province of Lower Silesia in individual months of the winter half-year (November–April, according to hydrological year classification) of 2005–2009. Data on air quality and meteorological elements came from the results of research conducted in the automatic net of air pollution monitoring conducted in the range of the State Environment Monitoring. The effect of meteorological elements on analysed pollutant concentration was determined using the correlation and regression analysis at significance level α < 0.05. The occurrence of maximum concentration of NO, NO2, NOX, SO2 and PM10 occurred in the coldest months during winter season (January, February and December) confirmed the strong influence of “low emission” on air quality. Among the meteorological factors assessed wind speed was most often selected component in step wise regression procedure, then air temperature, less air relative humidity and solar radiation. In the case of a larger number of variables describing the pollution in the atmosphere, in all analyzed winter seasons the most common set of meteorological elements were wind speed and air temperature.

An Error Analysis of LFM-II Forecasts During the 1982–1983 Winter Season

1984 ◽  
Vol 65 (10) ◽  
pp. 1073-1080
Author(s):  
Randy Schechter
Keyword(s):  
United States ◽  
Boundary Layer ◽  
Relative Humidity ◽  
Sea Level ◽  
Gulf Coast ◽  
Winter Season ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Temperature Boundary Layer ◽  
Temperature Boundary

An analysis of average errors (bias) in forecast variables of the National Meteorological Center (NMC) LFM-II model has been completed for the winter season of 1982–1983 (23 November 1982–31 March 1983). The forecast variables evaluated were sea level pressure, 1000-500 mb thickness, boundary layer temperature, boundary layer relative humidity, mean relative humidity, and boundary layer winds. Average errors were calculated from forecasts contained in the Forecast United States (FOUS) 60-78 bulletins for 89 stations around the United States and adjacent waters. Forecasts were stratified into runs initialized with 0000 GMT or 1200 GMT data. Verifications were produced for 12-, 24-, 36-, and 48-hour forecasts. The results substantiate certain geographically dependent error characteristics (biases) in the model. Sea level pressure is generally forecast too low east of the Rockies, particularly in the upper Mississippi valley, and too high west of the Rockies, particularly in the far southwest. Errors of 1000-500 mb thickness are generally small and tend to be forecast too high in the central United States and too low in the Rockies. For mean relative humidity, the model is too moist over the northern Rockies and too dry from Texas northward to Wisconsin. With the exception of the Rocky Mountain states, forecasts of boundary layer relative humidity are too dry nationwide. The largest dry bias is centered on the Texas Gulf coast. Boundary layer temperature forecasts are too warm along the Gulf coast and Florida and too cold near the Great Lakes. While errors are usually small for boundary layer winds, the error vectors have cyclonic curvature in the east and a northerly component in the nation's midsection. There is a pronounced diurnal variation in the model bias for some of the forecast variables examined (boundary layer temperature, boundary layer relative humidity, and, to a lesser degree, 1000-500 mb thickness). This is demonstrated by the differences in the average error fields at 0000 GMT and 1200 GMT.

scholarly journals Impact of CO2 concentration on indoor air quality and correlation with relative humidity and indoor air temperature in school buildings in Serbia

2016 ◽  
Vol 20 (suppl. 1) ◽  
pp. 297-307 ◽  
Author(s):  
Ivan Lazovic ◽  
Zarko Stevanovic ◽  
Milena Jovasevic-Stojanovic ◽  
Marija Zivkovic ◽  
Milos Banjac
Keyword(s):  
Carbon Dioxide ◽  
Air Quality ◽  
Relative Humidity ◽  
Air Temperature ◽  
Indoor Air ◽  
Negative Impact ◽  
Winter Season ◽  
Carbon Dioxide Concentration ◽  
Average Concentration ◽  
Heating Season

Previous studies have shown that poorly ventilated classrooms can have negative impact on the health of children and school staff. In most cases, schools in Serbia are ventilated naturally. Considering their high occupancy, classroom air quality test determines the level of air pollution, after which it is possible to implement corrective measures. The research presented in this study was conducted in four schools which are located in different areas and have different architecture designs. Measurements in these schools have been performed during the winter (heating season) and spring (non-heating season) and the following results were presented: indoor air temperature, relative humidity and carbon dioxide concentration. These results show that the classroom average concentration of carbon dioxide often exceeds the value of 1500 ppm, during its full occupancy, which indicates inadequate ventilation. Measurement campaigns show that carbon dioxide concentration increased significantly from non-heating to heating season in three of the four schools. Analysis of measurements also determined high correlation between relative humidity and carbon dioxide concentration in all schools in winter season. This fact may constitute a solid basis for the fresh air supply strategy.

Temperature and Flow Conditions in a Reservoir with a Submerged Outlet Tunnel During the Winter Period

1986 ◽  
Vol 17 (4-5) ◽  
pp. 399-406
Author(s):  
Arve M. Tvede
Keyword(s):  
Strong Influence ◽  
Winter Season ◽  
Circulation Pattern ◽  
Electricity Production ◽  
Winter Period ◽  
Flow Conditions ◽  
Power Station ◽  
Reservoir Water ◽  
Southern Norway ◽  
Tunnel Depth

The reservoir Sundsbarmvatn, in Southern Norway, is used for electricity production from November to May. Sundsbarmvatn has two main basins. Water from the upper basin, Mannerosfjorden, flows into the lower basin, Gullnesfjorden. The two basins are separated by a narrow sound with a sill. The regulation interval for Sundsbarmvatn is 612-574 m a.s.l., but the sill prevents Mannerosfjorden from being lowered below 580 m a.s.l. The water intake in Gullnesfjorden is 571 m a.s.l. The water temperature conditions has been studied during two winters when the reservoir water was released. This study shows that a marked thermocline was gradually developed at the depth of withdrawal in Gullnesfjorden. In the epilimnion layer the temperature is gradually lowered through the winter, but in the hypolimnion layer the temperature seems to stay constant through the winter. In Mannerosfjorden, however, we find no clear thermocline at the end of the winter. The remaining water was relatively warm with temperatures mainly above 3 °C. The sill between the two basins seems to have a strong influence on which depth the water is flowing out of Mannerosfjorden and hence on the temperature and circulation pattern in Gullnesfjorden. At the end of the winter season this flow is strengthening and initiates a homogeneous flow layer in Gullnesfjorden. This layer is dipping downwards towards the outlet tunnel. For this reason the temperature of the water leaving the power station is 0.4-1.2 °C colder than the hypolimnion temperature in the reservoir at the tunnel depth.

scholarly journals Feature and Language Selection in Temporal Symbolic Regression for Interpretable Air Quality Modelling

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 76
Author(s):  
Estrella Lucena-Sánchez ◽  
Guido Sciavicco ◽  
Ionel Eduard Stan
Keyword(s):  
Air Quality ◽  
Fundamental Problem ◽  
Multivariate Time Series ◽  
Quality Data ◽  
Data Sets ◽  
Air Quality Modelling ◽  
Regression Problem ◽  
Car Traffic ◽  
Pollutant Concentrations ◽  
Language Selection

Air quality modelling that relates meteorological, car traffic, and pollution data is a fundamental problem, approached in several different ways in the recent literature. In particular, a set of such data sampled at a specific location and during a specific period of time can be seen as a multivariate time series, and modelling the values of the pollutant concentrations can be seen as a multivariate temporal regression problem. In this paper, we propose a new method for symbolic multivariate temporal regression, and we apply it to several data sets that contain real air quality data from the city of Wrocław (Poland). Our experiments show that our approach is superior to classical, especially symbolic, ones, both in statistical performances and the interpretability of the results.

scholarly journals Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3338
Author(s):  
Ivan Vajs ◽  
Dejan Drajic ◽  
Nenad Gligoric ◽  
Ilija Radovanovic ◽  
Ivan Popovic
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Lower Accuracy ◽  
Wide Range ◽  
The Impact ◽  
Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

scholarly journals Evaluation of the AMPS Boundary Layer Simulations on the Ross Ice Shelf, Antarctica, with Unmanned Aircraft Observations

2017 ◽  
Vol 56 (8) ◽  
pp. 2239-2258 ◽  
Author(s):  
Jonathan D. Wille ◽  
David H. Bromwich ◽  
John J. Cassano ◽  
Melissa A. Nigro ◽  
Marian E. Mateling ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Relative Humidity ◽  
High Wind ◽  
Ice Shelf ◽  
High Wind Speed ◽  
Near Surface ◽  
Ross Ice Shelf ◽  
Low Cloud ◽  
The Antarctic

AbstractAccurately predicting moisture and stability in the Antarctic planetary boundary layer (PBL) is essential for low-cloud forecasts, especially when Antarctic forecasters often use relative humidity as a proxy for cloud cover. These forecasters typically rely on the Antarctic Mesoscale Prediction System (AMPS) Polar Weather Research and Forecasting (Polar WRF) Model for high-resolution forecasts. To complement the PBL observations from the 30-m Alexander Tall Tower! (ATT) on the Ross Ice Shelf as discussed in a recent paper by Wille and coworkers, a field campaign was conducted at the ATT site from 13 to 26 January 2014 using Small Unmanned Meteorological Observer (SUMO) aerial systems to collect PBL data. The 3-km-resolution AMPS forecast output is combined with the global European Centre for Medium-Range Weather Forecasts interim reanalysis (ERAI), SUMO flights, and ATT data to describe atmospheric conditions on the Ross Ice Shelf. The SUMO comparison showed that AMPS had an average 2–3 m s−1 high wind speed bias from the near surface to 600 m, which led to excessive mechanical mixing and reduced stability in the PBL. As discussed in previous Polar WRF studies, the Mellor–Yamada–Janjić PBL scheme is likely responsible for the high wind speed bias. The SUMO comparison also showed a near-surface 10–15-percentage-point dry relative humidity bias in AMPS that increased to a 25–30-percentage-point deficit from 200 to 400 m above the surface. A large dry bias at these critical heights for aircraft operations implies poor AMPS low-cloud forecasts. The ERAI showed that the katabatic flow from the Transantarctic Mountains is unrealistically dry in AMPS.

Inflight icing data gathering during routine flight operations — a case study

2001 ◽  
Vol 105 (1051) ◽  
pp. 501-516 ◽  
Author(s):  
A. P. Brown
Keyword(s):  
Data Gathering ◽  
Winter Season ◽  
Leading Edge ◽  
General Aviation ◽  
Data Sets ◽  
Large Droplet ◽  
Event Data ◽  
Flight Operations ◽  
Lower Corner

Abstract For the purpose of the design and certification of inflight icing protection systems for transport and general aviation aircraft, the eventual re-definition/expansion of the icing environment of FAR 25/JAR 25, Appendix C is under consideration. Such a re-definition will be aided by gathering as much inflight icing event data as reasonably possible, from widely-different geographic locations. The results of a 12-month pilot programme of icing event data gathering are presented. Using non-instrumented turboprop aircraft flying upon mid-altitude routine air transport operations, the programme has gathered observational data from across the British Isles and central France. By observing a number of metrics, notably windscreen lower-corner ice impingement limits, against an opposing corner vortex-flow, supported by wing leading edge impingement limits, the observed icing events have been classified as ‘small’, ‘medium’ or ‘large’ droplet. Using the guidance of droplet trajectory modelling, MVD values for the three droplet size bins have been conjectured to be 15, 40 and 80mm. Hence, the ‘large’ droplet category would be in exceedance of FAR/JAR 25, Appendix C. Data sets of 117 winter-season and 55 summer-season icing events have been statistically analysed. As defined above, the data sets include 11 winter and five summer large droplet icing encounters. Icing events included ‘sandpaper’ icing from short-duration ‘large’ droplets, and a singular ridge formation icing event in ‘large’ droplet. The frequency of ‘large’ droplet icing events amounted to 1 in 20 flight hours in winter and 1 in 35 flight hours in summer. These figures reflect ‘large’ droplet icing encounter probabilities perhaps substantially greater than previously considered. The ‘large’ droplet events were quite localised, mean scale-size being about 6nm.

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