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Free Space Optical (FSO) communication systems are gaining popularity due to its tremendous speed, advanced capacity, cost effectiveness, secure and easy to deploy wireless networks. This technology has proven an effective choice for last mile applications and hard to reach areas where deployment of optical fibre links is not feasible. But FSO link is highly weather dependent and as signal passes through the atmospheric channel, the main impairments are the atmospheric turbulence, which induce fading and deteriorate the system performance. Delhi has a great potential for FSO communication because of its clear skies. Since there is no analysis for weather condition found in Delhi, this work provides analysis of typical Delhi weather condition ranging from heavy to light rain, fog and clear sky. The performance of FSO link is analysed in terms of attenuation and link length margin under different weather condition. The results are concluded to identify which atmospheric condition influences more on FSO link performance.

This study advances our approach to modeling particulate matter levels—specifically, PM 10 and PM 2.5 —in Delhi’s dynamic urban environment through an extensive evaluation of traditional time series models (ARIMAX, SARIMAX) and machine learning models (RF, SVM) across air quality monitoring stations utilizing data from the period 2019 to 2023. We established a clear baseline of air quality variations using seasonal decomposition, highlighting critical seasonal peaks in PM 10 and PM 2.5 concentrations influenced by localized emissions and adverse weather conditions. Subsequent trend analysis revealed increasing PM 10 levels at several key monitoring stations, underscoring the impact of urban activities and seasonal variations. In contrast, reduction was observed in PM 2.5 levels at most monitoring stations. We utilized a wide range of exogenous variables, including other pollutants and meteorological parameters in our time series models to enhance the accuracy of predicting particulate matter. The SVM model proved to be more accurate in predicting particulate matter levels. It achieved testing RMSE values between 12.48 and 67.22 µg/m 3 for PM 10 and 8.38 and 48.95 µg/m 3 for PM 2.5 , with testing R -squared values between 0.30 and 0.95 for PM 10 and 0.41 and 0.96 for PM 2.5 . This research pioneers a methodologically enriched approach by systematically incorporating these exogenous factors, enhancing predictive capabilities, and deepening the understanding of complex environmental dynamics specific to urban cities like Delhi. The extensive spatial coverage and robust integration of diverse exogenous factors can significantly enhance environmental modeling, providing actionable insights for policymakers and advancing air quality forecasting in urban megacities.

Delhi-National Capital Region (Delhi-NCR) is prone to repeated episodes of severe air pollution, triggered by high air pollution emissions, unfavorable meteorological conditions and episodic regional air pollution transport. To fight acute episodes of pollution, the Graded Response Action Plan (GRAP) was adopted as a graded emergency response plan with progressively higher levels of intervention based on worsening levels of Air Quality Index (AQI). However, there is yet little systematic evidence of the stage-wise performance of GRAP in real-world atmospheric conditions. This study shows a stage-wise and multi-pollutant assessment of GRAP using continuous monitoring data from nine officially classified pollution hotspots in Delhi-NCR. Concentrations of PM 2.5 , PM 10 , NO 2 , NO x , CO, O 3 , SO 2 , NH 3 and selected VOCs were analyzed during pre-GRAP, GRAP and post-GRAP (September 2023 - March 2024) phase along with key meteorological parameters. Statistical analyses included descriptive statistics, analysis of variances (ANOVA), Pearson correlation, and principal component analysis. Results indicated that concentrations of PM 2.5 were consistently high during higher stages of GRAP despite increases in control measures, with significant linkages ( p  < 0.05) between pollutant accumulation and weak dispersion conditions with low wind speed and reduced mixing. In contrast, ozone concentrations during and following the enforcement of GRAP showed signs of a non-linear photochemical response in a VOC-limited regime. Overall, the results show that GRAP provides a limited and meteorology-dependent, short-term relief which is limited by reactive activation, the formation of secondary pollutants and unfavorable dispersion conditions. Integrating meteorology-informed forecasting, stage-specific targeting and region wise co-ordination is proposed for improved emergency air quality management in Delhi-NCR.

The direct aerosol-radiative effects in the WRF-Chem model account for scattering/absorption of solar radiation due to aerosols, while aerosol–cloud interactions result in modifying wet scavenging of the ambient concentrations as an indirect aerosol effect. In this study, impact of aerosol on meteorological parameters, PM10 and ozone concentrations are analysed which revealed (i) that a net decrease in shortwave and longwave radiation by direct feedback results in decrease in temperature up to 0.05 K, (ii) that a net increase due to longwave and shortwave radiation when both direct and indirect effects are taken together results in an increase in temperature up to 0.25 K (where the mean of temperature is 33.5 °C and standard deviation 2.13 °C), (iii) a marginal increase in boundary layer height of 50 m with increase in temperature with feedbacks, (iv) overall net increase in radiation by direct and indirect effect together result in an increase in PM10 concentration up to 12 μg m−3 (with PM10 mean as 84.5 μg m−3 and standard deviation 28 μg m−3) and an increase in ozone concentration up to 3 μg m−3 (with ozone mean as 29.65 μg m−3 and standard deviation 5.2 μg m−3) mainly due to net increase in temperature. Furthermore, impact of sensitivity of different aerosol mechanisms on PM10 concentrations was scrutinized for two different mechanisms that revealed underestimation by both of the mechanisms with MOSAIC scheme, showing less fractional bias than MADE/SORGAM. For the dust storm period, MOSAIC scheme simulated higher mass concentrations than MADE/SORGAM scheme and performed well for dust-storm days while closely capturing the peaks of high dust concentrations. This study is one of the first few to demonstrate the impact of both direct and indirect aerosol feedback on local meteorology and air quality using a meteorology–chemistry modelling framework; the WRF-Chem model in a tropical urban airshed in India located in semi-arid climatic zone. It is inferred that semi-arid climatic conditions behave in a vastly different manner than other climatic zones for direct and indirect radiative feedback effects.

In the current scenario of the deregulated Indian electricity market where the power demand and its availability vary remarkably, the factors playing a significant role in demand variations are often associated with the impact of unprecedented weather conditions and technological evolutions. To maintain grid security and discipline that yield to financial implications, there lies a great need to formulate an equilibrium between electricity supply and demand. Devising a model to anticipate the variations which are highly adaptive to such changes is the need of the hour. For this purpose, an algorithm has been proposed in this study, which is best suited for the day-ahead load forecast. The variables selected for the forecast are one-day-lagged demand statistics, seasonality trend, weather, and calendar variables. The proposed algorithm outperforms the existing benchmark model, which is evaluated through various statistical performance metrics such as mean absolute percentage error, mean absolute error, root-mean-square error, and coefficient of variation. The performance of the proposed methodology at the seasonal level is analysed and validated through uncertainty analysis with one post-sample year for the state of Delhi, India. This model presents its compatibility to prevalent grid regulations as well as shall hold good in the weather and demand variations possibly expected in the future.

Delhi, the capital city of India witnesses severe degradation of air quality and rapid enhancement of trace gases during winter. Still it is unclear about the relative role of the meteorological conditions and the post-monsoon agricultural stubble burning on the occurrence of these events. To overcome this, we examine the use of applying high-resolution transport model to establish the link between atmospheric concentrations and upstream surface fluxes. This study reports the implementation of a Lagrangian approach and demonstrates its capability in deriving the upwind influences over Delhi. We simulate stochastic back trajectories over Delhi by implementing stochastic time-inverted Lagrangian transport (STILT) model, driven by the meteorological fields from the European Centre for Medium Range Weather Forecasts (ECMWF) model. During the post-monsoon, when mixing layer height is shallow, we find high near-field influence. The variations in footprint simulations with receptor heights show the effect of mixing layer dynamics on the air-parcels. By using atemporal emission fields, we find a considerable impact of meteorological conditions during November that contributes to the enhancements of trace gases. Together with strong emissions (anthropogenic and biomass burning), these enhancements can be several orders higher compared to other seasons. Through the receptor-oriented STILT implementation over India, we envision a wide range of applications spanning from air quality to climate change. An advantage of this implementation is that it allows the use of pre-calculated footprints in simulating any trace gas species and particulate matter, making it computationally less demanding than running an ensemble of full atmospheric transport model. Research highlights Our study elaborates a method to estimate the near-field influences on a region of interest or receptor location (Delhi), which is pivotal in devising mitigation strategies to curb the increasing pollution events (one of the country's greatest concerns).For this, we implement STILT that uses ECMWF meteorological data to generate simulations of realistic atmospheric trajectories and footprints to the Indian subcontinent domain. From this data, the influence matrix is derived. We demonstrate the usefulness of the STILT modeling framework by deriving air-parcel trajectories and footprint over Delhi, which shows a higher influence of Haryana and Punjab region as upwind location to Delhi during the pre-monsoon and post-monsoon season. We highlight the importance of proper accounting of vertical mixing in the atmospheric model by simulating footprint and trajectory measurements at different heights. This shows that during postmonsoon when PBL height is shallow there is a higher influence function thus choking Delhi in the winter. Our analysis shows the usefulness of pre-calculated footprints in simulating any trace gas species and particulate matter concentration thus saving a lot of computational costs incurred. This is illustrated by generating concentration signals using EDGAR global inventory for CO and CO2 emissions from biomass burning and CO 2 , CO, N 2 O and CH 4 from anthropogenic emissions. We observed concentration enhancement in agreement with the diurnal PBL dynamics (higher in lower layers (20m) compared to higher layers (500m)). We envision extending the STILT network to have wider insights on regional and local fluxes of CO 2 , CO, and CH 4 . The study can be improvised by utilizing hourly-varying emission fluxes and also by accounting for other sources of emissions.