Diffusion from a point source using power law of wind speed

bl 'kks/k i= esa ,d 'kgjh ok;q eaMy dks ,d lzksr fcUnq eku dj inkFkZ ds folj.k ds fy, ,d ekWMy laLFkkfir fd;k x;k gSA blds fiPNd dks ,d ifjHkkf"kr voLFkk ekuk x;k gS tgk¡ bldh lkanzrk 'kwU; gks tkrh gSA LFkkf;Ro Jsf.k;ksa dh miyC/k rduhdksa }kjk ikoj ykW dk mi;ksx djrs gq, m/okZ/kj iou vi:i.k dks vkdfyr fd;k x;k gSA bl ekWMy esa vkdfyr lkanzrkvksa dh rqyuk vUos"k.kdrkZvksa ds QhYM izs{k.kksa ls izkIr fd, x, fu"d"kksZa ds lkFk dh xbZ gSA In the present paper, a model for the diffusion of material from a point source in an urban atmosphere is incorporated. The plume is assumed to have a well-defined edge at which the concentration falls to zero. The vertical wind shear is estimated using power law, by employing most of the available techniques of stability categories. The concentrations estimated from the model were compared favorably with the field observations of investigators.

Simulation Model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RND v1.0

Nitrous acid (HONO), one of the reactive nitrogen oxides (NOy), plays an important role in the formation of ozone (O3) and fine aerosols (PM2.5) in the urban atmosphere. In this study, a simulation model of Reactive Nitrogen species using Deep neural network model (RND) was constructed to calculate the HONO mixing ratios through a deep learning technique using measured variables. A Python-based Deep Neural Network (DNN) was trained, validated, and tested with HONO measurement data obtained in Seoul during the warm months from 2016 to 2019. A k-fold cross validation and test results confirmed the performance of RND v1.0 with an Index Of Agreement (IOA) of 0.79 ~ 0.89 and a Mean Absolute Error (MAE) of 0.21 ~ 0.31 ppbv. The RNDV1.0 adequately represents the main characteristics of HONO and thus, RND v1.0 is proposed as a supplementary model for calculating the HONO mixing ratio in a high- NOx environment.

Different solutions of the diffusion equation and its applications

In this report, we solved the advection–diffusion equation under pollutants deposition on the ground surface, taking wind speed and vertical diffusion depend on the vertical height. Also, we estimated a simple diffusion model from point source in an urban atmosphere and the conservative material with downwind was evaluated. Then, we calculated the extreme ground-level concentration as a function of stack height and plume rise in two cases. Comparison between the proposed models and the emission from the Egyptian Atomic Research Reactor at Inshas had been done. Lastly, we discussed the results in this report.

Evaluation of the contribution of new particle formation to cloud droplet number concentration in the urban atmosphere

The effect of new particle formation (NPF) on cloud condensation nuclei (CCN) varies widely in diverse environments. CCN or cloud droplets from NPF sources remain highly uncertain in the urban atmosphere; they are greatly affected by the high background aerosols and frequent local emissions. In this study, we quantified the effect of NPF on cloud droplet number concentration (CDNC, or Nd) at typical updraft velocities (V) in clouds based on field observations on 25 May–18 June 2017 in urban Beijing. We show that NPF increases the Nd by 32 %–40 % at V=0.3–3 m s−1 during the studied period. The Nd is reduced by 11.8 ± 5.0 % at V=3 m s−1 and 19.0 ± 4.5 % at V=0.3 m s−1 compared to that calculated from constant supersaturations due to the water vapor competition effect, which suppresses the cloud droplet formation by decreasing the environmental maximum supersaturation (Smax). The effect of water vapor competition becomes smaller at larger V that can provide more sufficient water vapor. However, under extremely high aerosol particle number concentrations, the effect of water vapor competition becomes more pronounced. As a result, although a larger increase of CCN-sized particles by NPF events is derived on clean NPF days when the number concentration of preexisting background aerosol particles is very low, no large discrepancy is presented in the enhancement of Nd by NPF between clean and polluted NPF days. We finally reveal a considerable impact of the primary sources on the evaluation of the contribution of NPF to CCN number concentration (NCCN) and Nd based on a case study. Our study highlights the importance of full consideration of both the environmental meteorological conditions and multiple sources (i.e., secondary and primary) to evaluate the effect of NPF on clouds and the associated climate effects in polluted regions.