Manju Mohan

Analysis of various schemes for the estimation of atmospheric stability classification

Abstract Atmospheric stability classification is required to quantify the dispersion capabilities of the ambient atmosphere (i.e. the dispersion coefficients or the standard deviation of concentration distribution in lateral and vertical directions, σ y and σ z , respectively) in the air quality models for concentration predictions. Several different types of stability classification schemes are given depending on the availability of meteorological parameters and the related atmospheric processes in the lower part of the boundary layer. In the present study, atmospheric stabilities are estimated from seven different stability classification schemes based on Monin–Obukhov length, bulk and gradient Richardson numbers, temperature gradient, wind speed ratio, etc. The estimated stabilities are compared with the Pasquill stability classification which is very widely used and requires only routine data. The spread of stabilities in each scheme is large and sometimes it covers the entire range of stabilities for a given Pasquill class. However, it has been observed that the schemes based on Monin–Obukhov length and Richardson number gives reasonable comparison than the rest of the schemes. The performance of each of these schemes has been discussed based on the underlying physics. The study demonstrates that for a complete and exhaustive data set such as Kincaid, there exists a wide variations among all atmospheric stabilities with Pasquill and one another in a manner such that an entire range of stabilities may be obtained for a given Pasquill class. The implications of this on estimated distance to maximum ground level concentrations are great and could affect the overall outcome of conventional Gaussian models for industrial siting, planning and management.

Analysis of WRF Model Performance over Subtropical Region of Delhi, India

Model performance and sensitivity to model physics options are studied with the Weather Research and Forecasting model (version 3.1.1) over Delhi region in India for surface and upper air meteorological parameters in summer and winter seasons. A case study with the model has been performed with different configurations, and the best physics options suited for this region have been, determined. Comparison between estimated and observed data was carried out through standard statistical measures. Generally, the combination of Pleim-Xiu land surface model, Pleim surface layer scheme, and Asymmetric Convective Model has been found to produce better estimates of temperature and relative humidity for Delhi region. Wind speed and direction estimations were observed best for MM5 similarity surface layer along with Yonsei University boundary layer scheme. Nested domains with higher resolutions were not helpful in improving the simulation results as per the current availability of the data. Overall, the present case study shows that the model has performed reasonably well over the subtropical region of Delhi.

Observed regional distribution of sulfur dioxide in Asia

SO2 concentrations have been measured for one year at forty-five locations throughout Asia using passive samplers. Duplicate samples were exposed at each site for one month intervals. The sites were selected to provide background information on the distribution of SO2 over wide geographical regions, with emphasis on the regional characteristics around areas estimated to be sensitive to sulfur deposition. The annual mean values ranged from less than 0.3 μg/m3 at Tana Rata, located at 1545 m on the Malaysia Peninsula, Lawa Mandau, (Borneo) Malaysia, and Dhankuta, Nepal, to values greater than 20 μg/m3 at Luchongguan (Guiyang) China, Babar Mahal, Nepal, and Hanoi, Vietnam. In general high concentrations were measured throughout China, with the highest concentrations in the heavy industrial areas in Guiyang. The concentrations in east Asia around the Korea peninsula were ∼5 μg/m3. The concentrations in the southeast Asia tropics were low, with no station in Malaysia and Indonesia having average concentrations exceeding 1.7 μg/m3. The observed SO2 concentrations were found to display a distinct seasonal cycle which is strongly influenced by the seasonality of winds and precipitation patterns.

Development of dense gas dispersion model for emergency preparedness

Mathematical models are recognized as important tools for providing quantitative assessment of the consequences of the aeodental release of hazardous materials. In several accidental release situations, denser-than-air vapour clouds are formed which exhibit dispersion behavlour markedly different from that observed for passive atmospheric pollutants. The present work undertakes the development and validation of conceptually simple and computationally efficient dense gas dispersion models which could be used for emergency response. Here, IIT Heavy Gas Models I and II have been developed for instantaneous and continuous releases, respectively, of dense toxic materials in the atmosphere. Sensitivity tests have been performed to determine the various empirical coefficaents which are found to be quite different than those used in the earlier studies. Particular emphasis has been laid on model validation by comparing their performance against relevant field trial data (Thorney Island, Burro Series and Maplin Sands Trials) as well as with other models. On the basis of statistical evaluation, a good performance of the model has been established. The performance of the IIT Heavy Gas Model is close to the model showing the best performancc amongst 11-14 other models developed in various countries. Using the IIT Heavy Gas Model, the Safe disl ance/vulnerable zones can be easily estimated for different meteorological and release condi- tions for the; storage of various hazardous chemicals. Key word index: Gravity slumping, air entrainment, cloud heating, sensitivity analysis, model validation, statistical evaluation, vulnerable zones.

Visibility degradation during foggy period due to anthropogenic urban aerosol at Delhi, India

Fog occurs more frequently over urban areas than rural areas. It may occur due to increased air pollution emanating from variety of sources in the urban areas. The increased pollution levels may lead to the atmospheric reactions resulting into the formation of secondary pollutants that may also lead to the needed cloud condensation nuclei. Northern regions of India experience severe foggy conditions during the winter period (November–January) each year. In this study, we have simultaneously measured the particulate mass concentration (0.23 µm to 20 µm), meteorological parameters and atmospheric visibility in Mega city Delhi during a winter month of the years 2007– 2008 in order to have an improved understanding of their role in fog formation. The effects of aerosols on fog formation are discussed through an analysis of trends in fog frequency and comparison with meteorological parameters, and visibility as an indicator of aerosol load. This satisfies the precondition for using these relations. The association between the meteorological parameters (visibility, depression temperature) and air pollutants are examined. The Windows software SPSS (version 17.0) is used to fit a linear regression model. The model explained the variation in visibility due to depression temperature and aerosols load.

AN EVALUATION OF DISPERSION COEFFICIENTS FOR USE IN AIR QUALITY MODELS

Standard deviations of concentration in horizontal andvertical directions i.e. σy andσz have been estimated by using fivedifferent schemes based on empirical(due to Pasquill and Briggs)schemes and sophisticated methods(due to Irwin, Draxler, Taylor, Hanna et al.). The fiveschemes are discussed atlength. The purpose of this study is to make use ofmeteorological observations whichare routinely available, to test all the above methods andintercompare the resultswith one another and observations so that the sensitivityof each schemeunder various atmospheric stability conditions could beassessed. It has beenfound that the existing schemes are good enough to providereasonable estimates ofdispersion coefficient (σy) during highly unstableconditions (Pasquill stability classes A and B). However, thesame is not true for the case when the stability increasesfrom C to F and turbulencedecreases, specifically during stable atmospheric conditions,when the observedvalues were found to be much higher than all the existingschemes. This suggests thatwhile we continue to use the current methods of estimatingthe dispersion parameters,a rigorous search is required for methods which give predictionswhich are close-to-realityduring such conditions which are represented by lowlevels (in terms of magnitude)of atmospheric turbulence leading to higher levelsof pollution.As one of the sophisticated methods requiresthe use of σv and σw (standard deviationsof wind velocity fluctuation in y and z directions),these have been estimated andvalidated with observed data (field experiments conductedby EPRI at Kincaid).Statistical evaluation of σv and σwbased on performance measures indicate a goodperformance of the parameterisations adopted in thisstudy. In the case of σw duringunstable conditions a comparison of three differentschemes with observations is made.

Assessment of contribution to PM10 concentrations from long range transport of pollutants using WRF/Chem over a subtropical urban airshed

Abstract A regional chemical transport model was implemented to simulate the Respirable Suspended Particulate Matter (PM 10 ) concentration in order to study the impact of long–range transport of air pollutants over megacity Delhi with due consideration to different geographical domains extending up to entire Asia and corresponding emissions. PM 10 concentration levels over megacity Delhi remain persistently high, often exceeding the ambient air quality standards. A chemical transport model namely Weather Research and Forecasting (WRF) model Version 3.2 coupled with chemistry module (WRF/Chem) was utilized with nested domains for this purpose, subsequent to model evaluation for the period during June, 2010 that includes extremely high PM 10 concentrations. A highly satisfactory model performance was interpreted based on the several statistical parameters as per the current state of the science and their recommended values. Based on model simulations representing different geographical domains encompassing Asia, India, North India and Delhi and their corresponding emissions, it was clearly reflected that contributions due to emissions of the megacity Delhi alone is 11%–41% and thus remaining (59%–89%) proportion is expected to be contributed from the sources outside of the Delhi region which is significant. It is demonstrated that the WRF/Chem model performs well for a sub–tropical urban airshed though there is scope of improvement for the consistent under– prediction with more refined emission inventories. Nevertheless, this model could be implemented to assess the long– range transport of pollutants so as to adequately address the influence of the remote sources outside the urban airshed. This can serve as an important tool towards planning and implementing the regulatory policies for air pollution control for more effective outcomes.

Integrated risk analysis for acute and chronic exposure to toxic chemicals

The traditional practice to assess and evaluate different types of risk in isolation to each other are liable to give erroneous results. Integrated risk assessment is an answer to overcome this problem. This paper presents the cumulative or integrated assessment of acute risk posed by accidental release of hazardous chemical (e.g. chlorine) and chronic risk induced by toxic chemicals (e.g. cadmium, chromium and nickel) present in the ambient environment. The present study has been carried out in a most simplified way to demonstrate and appreciate the broader context of integrated risk analysis (IRA). It has been observed that the inclusion of background risk factors (BRF) in individual risk factors (IRF) related to an industry may significantly alter the siting and planning strategies of that industry.

Multirule Based Diagnostic Approach for the Fog Predictions Using WRF Modelling Tool

The prediction of fog onset remains difficult despite the progress in numerical weather prediction. It is a complex process and requires adequate representation of the local perturbations in weather prediction models. It mainly depends upon microphysical and mesoscale processes that act within the boundary layer. This study utilizes a multirule based diagnostic (MRD) approach using postprocessing of the model simulations for fog predictions. The empiricism involved in this approach is mainly to bridge the gap between mesoscale and microscale variables, which are related to mechanism of the fog formation. Fog occurrence is a common phenomenon during winter season over Delhi, India, with the passage of the western disturbances across northwestern part of the country accompanied with significant amount of moisture. This study implements the above cited approach for the prediction of occurrences of fog and its onset time over Delhi. For this purpose, a high resolution weather research and forecasting (WRF) model is used for fog simulations. The study involves depiction of model validation and postprocessing of the model simulations for MRD approach and its subsequent application to fog predictions. Through this approach model identified foggy and nonfoggy days successfully 94% of the time. Further, the onset of fog events is well captured within an accuracy of 30–90 minutes. This study demonstrates that the multirule based postprocessing approach is a useful and highly promising tool in improving the fog predictions.

Analysis of air pollution during a severe smog episode of November 2012 and the Diwali Festival over Delhi, India

The hazardous combination of smoke and pollutant gases, smog, is harmful for health. The harmful smog episodes over London, the Meuse Valley, and Donora are some of the well-known pollution episodes formed due to the mixture of smoky fumes and adverse meteorological conditions. A severe smog episode was observed over Delhi, India, during November 2012, resulting in very low visibility and various respiratory problems. Very high values of pollutants (particulate matter, PM10 as high as 989 µg m−3, PM2.5 as high as 585 µg m−3, and nitrogen dioxide as high as 540 µg m−3) were measured all over Delhi during the smog episode. In the study done, episodes of different nature and intensity are analysed based on remote-sensing data for 3 years (2010–2012): one of regional origin (the Delhi smog episode of 2012) and another of local origin (Diwali). Remote-sensing and in situ data have revealed an insight into the genesis and temporal and spatial variance during these episodes. Extensive use of satellite-derived parameters such as fire maps, the ultra violet aerosol index from the Aura satellite, and aerosol optical depth is made in the present study along with the output trajectories from the Hybrid Single-Particle Lagrangian-Integrated Trajectory model and in situ data. It is observed that during the smog episode all the aerosol optical depth, ultra violet aerosol index, PM2.5, and PM10 values surpassed those of the Diwali period (which in itself is a major dreaded annual air pollution event in the city) by a considerable amount at all stations across Delhi. The parameters used from the remote-sensing data and the ground-based observations at various stations across Delhi are very well in agreement with the intensity of smog episodes. The analysis clearly shows that regional pollution can have a greater contribution towards deteriorating air quality than local pollution under adverse meteorological conditions and is in agreement with other similar studies over Delhi.