Emerging airborne contaminants in India : Platinum Group Elements from catalytic converters in motor vehicles

© The Author(s), 2016. This is the author's version of the work and is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Applied Geochemistry 75 (2016): 100-106, doi:10.1016/j.apgeochem.2016.10.006.Platinum Group Element (PGE) pollution on the Indian subcontinent is a growing concern because vehicle sales in India have rapidly increased over the last decade, and it is well known that automobile catalytic converters are one of the major source of anthropogenic PGE in the environment. Despite the rapid growth of the Indian automobile industry, the sources and magnitude of PGE contamination in Indian airborne particles are unknown. In this study we report PGE and mercury (Hg) concentrations, as well as osmium isotope ratios (187Os/188Os) of airborne particles (PM10) collected in Kanpur, a large industrial city in India. We estimate that 61±22%, 32±24%, and 7±3% of the total Os fraction are derived from eroding upper continental crust, catalytic converters fitted in the exhaust system of motor vehicles, and fossil fuel combustion, respectively. Only one sample had a ten times higher (~76%) than average contribution from fossil fuel. Unlike Os, Pt is predominantly (84±10%) derived from anthropogenic sources. Platinum Group Element and Hg concentrations are not well correlated. However, the highest concentration of particulate Hg corresponds to the most radiogenic 187Os/188Os isotope ratios (4.6). Our results further indicated that PGE/Ir ratios could be successfully used to quantify the relative proportions of natural and anthropogenic PGE sources in aerosol samples. Since PGE and Hg data on Indian environmental samples are scarce, this study provides an interpretive framework that calls for additional assessments of PGE and Hg concentrations in environmental samples from India.I.S. acknowledges an Indian Institute of Technology Kanpur Initiation Grant that supported this research.2018-10-2

Biases in model-simulated surface energy fluxes during the Indian monsoon onset period

We use eddy-covariance measurements over a semi-natural grassland in the central Indo-Gangetic Basin to investigate biases in energy fluxes simulated by the Noah land-surface model for two monsoon onset periods: one with rain (2016) and one completely dry (2017). In the preliminary run with default parameters, the offline Noah LSM overestimates the midday (1000–1400 local time) sensible heat flux (H) by 279% (in 2016) and 108% (in 2017) and underestimates the midday latent heat flux (LE) by 56% (in 2016) and 67% (in 2017). These discrepancies in simulated energy fluxes propagate to and are amplified in coupled Weather Research and Forecasting model simulations, as seen from the High Asia Reanalysis dataset. One-dimensional Noah simulations with modified site-specific vegetation parameters not only improve the partitioning of the energy fluxes (Bowen ratio of 0.9 in modified run versus 3.1 in the default run), but also reduce the overestimation of the model-simulated soil and skin temperature. Thus, use of ambient site parameters in future studies is warranted to reduce uncertainties in short-term and long-term simulations over this region. Finally, we examine how biases in the model simulations can be attributed to lack of closure in the measured surface energy budget. The bias is smallest when the sensible heat flux post-closure method is used (5.2 W m −2 for H and 16 W m −2 for LE in 2016; 0.17 W m −2 for H and 2.8 W m −2 for LE in 2017), showing the importance of taking into account the surface energy imbalance at eddy-covariance sites when evaluating land-surface models

Numerical simulation and evaluation of global ultrafine particle concentrations at the Earth's surface

A new global dataset of annually averaged ultrafine particle (UFP) concentrations at the Earth's surface for the years 2015–2017 has been developed through numerical simulations using the ECHAM/MESSy Atmospheric Chemistry model (EMAC). We present total and size-resolved concentrations along with their interannual variability. Size distributions of emitted particles from the contributing source sectors have been derived based on literature reports. The model results of UFP concentrations are evaluated using particle size distribution and particle number concentration measurements from available datasets and the literature. While we obtain reasonable agreement between the model results and observations (logarithmic-scale correlation of r=0.76 for non-remote, polluted regions), the highest values of observed, street-level UFP concentrations are systematically underestimated, whereas in rural environments close to urban areas the model generally overestimates observed UFP concentrations. As the relatively coarse global model does not resolve concentration gradients in urban centres and industrial UFP hotspots, high-resolution data of anthropogenic emissions are used to account for such differences in each model grid box, obtaining UFP concentrations with unprecedented 0.1∘×0.1∘ horizontal resolution at the Earth's surface. This observation-guided downscaling further improves the agreement with observations, leading to an increase in the logarithmic-scale correlation between observed and simulated UFP concentrations to r=0.84 in polluted environments (and 0.95 in all regions), a decrease in the root mean squared logarithmic error (from 0.57 to 0.43), and removal of discrepancies associated with air quality and population density gradients within the model grid boxes. The model results are made publicly available for studies on public health and other impacts of atmospheric UFPs, as well as for intercomparison with other regional and global models and datasets.</p

The Indian COSMOS Network (ICON): validating L-band remote sensing and modelled soil moisture data products

Availability of global satellite based Soil Moisture (SM) data has promoted the emergence of many applications in climate studies, agricultural water resource management and hydrology. In this context, validation of the global data set is of substance. Remote sensing measurements which are representative of an area covering 100 m2 to tens of km2 rarely match with in situ SM measurements at point scale due to scale difference. In this paper we present the new Indian Cosmic Ray Network (ICON) and compare it’s data with remotely sensed SM at different depths. ICON is the first network in India of the kind. It is operational since 2016 and consist of seven sites equipped with the COSMOS instrument. This instrument is based on the Cosmic Ray Neutron Probe (CRNP) technique which uses non-invasive neutron counts as a measure of soil moisture. It provides in situ measurements over an area with a radius of 150–250 m. This intermediate scale soil moisture is of interest for the validation of satellite SM. We compare the COSMOS derived soil moisture to surface soil moisture (SSM) and root zone soil moisture (RZSM) derived from SMOS, SMAP and GLDAS_Noah. The comparison with surface soil moisture products yield that the SMAP_L4_SSM showed best performance over all the sites with correlation (R) values ranging from 0.76 to 0.90. RZSM on the other hand from all products showed lesser performances. RZSM for GLDAS and SMAP_L4 products show that the results are better for the top layer R = 0.75 to 0.89 and 0.75 to 0.90 respectively than the deeper layers R = 0.26 to 0.92 and 0.6 to 0.8 respectively in all sites in India. The ICON network will be a useful tool for the calibration and validation activities for future SM missions like the NASA-ISRO Synthetic Aperture Radar (NISAR)

Cosmic-ray soil water monitoring: the development, status & potential of the COSMOS-India network

Soil moisture (SM) plays a central role in the hydrological cycle and surface energy balance and represents an important control on a range of land surface processes. Knowledge of the spatial and temporal dynamics of SM is important for applications ranging from numerical weather and climate predictions, the calibration and validation of remotely sensed data products, as well as water resources, flood and drought forecasting, agronomy and predictions of greenhouse gas fluxes. Since 2015, the Centre for Ecology and Ecology has been working in partnership with several Indian Research Institutes to develop COSMOS-India, a new network of SM monitoring stations that employ cosmic-ray soil moisture sensors (CRS) to deliver high temporal frequency, near-real time observations of SM at field scale. CRS provide continuous observations of near-surface (top 0.1 to 0.2 m) soil volumetric water content (VWC; m3 m-3) that are representative of a large footprint area (approximately 200 m in radius). To date, seven COSMOS-India sites have been installed and are operational at a range of locations that are characterised by differences in climate, soil type and land management. In this presentation, the development, current status and future potential of the COSMOS-India network will be discussed. Key results from the COSMOS-India network will be presented and analysed

Revisited modeling of Titan’s middle atmosphere electrical conductivity

International audienceThe atmospheric electrical conductivity measured by the Permittivity, Wave and Altimetry (PWA) subsystem on board the Huygens probe, during the landing mission on Titan, has been modeled in the present work. Previous modeling studies showed a Galactic Cosmic Ray (GCR) peak of conductivity at a higher altitude and a quantitative overestimation in the altitude range 0–100 km compared to that observed by the PWA instrument. Recently the PWA data was revisited and provided new constraints on the conductivity at altitudes 100–180 km. Because the aerosols in the atmosphere are known to alter the electron concentration, using a detailed distribution of the aerosols at all altitudes, the electron conductivity has been calculated in the altitude range 0–180 km. By using a variable range of photoemission threshold for the aerosols, the present model is able to reasonably predict the altitude at which the GCR peak of conductivity occurs and to meet the new constraints for the conductivity profile

Identification of sources affecting fog formation using receptor modeling approaches and inventory estimates of sectoral emissions

Positive matrix factorization (PMF) was used to identify factors affecting fog formation in Kanpur during the ISRO-GBP land campaign-II (LC-II) in December 2004. PMF predicted factors were validated by contrasting the emission strength of sources in the foggy and clear periods, using a combination of potential source contribution function (PSCF) analysis and quantitative emission inventory information. A time series aerosol chemical data set of 29 days and 12 species was decomposed to identify 4-factors: Secondary species, Biomass burning, Dust and Sea salt. PMF predicted particle mass with a satisfactory goodness-of-fit (slope of 0.83 ± 0.17 and R2 of 0.8), and strong species within 11–12% relative standard deviation. Mean contributions of anthropogenic factors were significantly higher during the foggy period for secondary species (2.9 ± 0.3) and biomass burning (1.2 ± 0.09) compared to the clear period. Local sources contributing to aerosols that mediated fog events at Kanpur, based on emissions in a 200 km × 200 km area around Kanpur city were thermal power plants and transportation (SO2) and biofuel combustion (BC and OM). Regional scale sources influencing emissions during the foggy period, in probable source regions identified by PSCF included thermal power plants, transportation, brick kilns and biofuel combustion. While biofuel combustion and transportation are distributed area sources, individual point sources include coal-fired thermal power plants located in Aligarh, Delhi, Ghaziabad, Jhansi, Kanpur, Rae Bareli and Rupnagar and brick kilns located in Allahabad, Agra, Farrukhabad, Ghaziabad, Kanpur, Ludhiana, Lucknow and Rae Bareli. Additionally, in the foggy period, large areas of probable source regions lay outside India, implying the significance of aerosol incursion from outside India.© Elsevie

Quantifying the importance of galactic cosmic rays in cloud microphysical processes

Galactic Cosmic Rays are one of the major sources of ion production in the troposphere and stratosphere. Recent studies have shown that ions form electrically charged clusters which may grow to become cloud droplets. Aerosol particles charge by the attachment of ions and electrons. The collision efficiency between a particle and a water droplet increases, if the particle is electrically charged, and thus aerosol-cloud interactions can be enhanced. Because these microphysical processes may change radiative properties of cloud and impact Earth's climate it is important to evaluate these processes' quantitative effects. Five different models developed independently have been coupled to investigate this. The first model estimates cloud height from dew point temperature and the temperature profile. The second model simulates the cloud droplet growth from aerosol particles using the cloud parcel concept. In the third model, the scavenging rate of the aerosol particles is calculated using the collision efficiency between charged particles and droplets. The fourth model calculates electric field and charge distribution on water droplets and aerosols within cloud. The fifth model simulates the global electric circuit (GEC), which computes the conductivity and ionic concentration in the atmosphere in altitude range 0–45 km. The first four models are initially coupled to calculate the height of cloud, boundary condition of cloud, followed by growth of droplets, charge distribution calculation on aerosols and cloud droplets and finally scavenging. These models are incorporated with the GEC model. The simulations are verified with experimental data of charged aerosol for various altitudes. Our calculations showed an effect of aerosol charging on the CCN concentration within the cloud, due to charging of aerosols increase the scavenging of particles in the size range 0.1 µm to 1 µm

"Traffic intervention" policy fails to mitigate air pollution in megacity Delhi

Megacity Delhi has been ranked amongst the top most polluted cities in the world consistently over the last few years (WHO, 2016). As a desperate and emergency measure, the administration implemented 'traffic intervention' mitigation effort by instigating 'odd-even' policy as a trial for 15 days in January (1-15) 2016. During this period, odd and even numbered private cars were restricted to respective odd and even days. Here we examine the impact of this policy intervention on ambient particulate matter smaller than 2.5 mu m (PM2.5) through a combination of in-situ, satellite and model data. Traffic restriction reduces PM2.5 by 4-6% (maximum up to 10% in three local hotspots) which is within the uncertainty range of satellite-based estimates (and hence not detected). This is not a significant result considering the fact that such step was taken as an emergency measure when PM(2.5)exposure exceeded 250 mu g/m(3) during the winter season. The failure is attributed to stable meteorological conditions (winds are not strong enough to disperse PM2.5 away) during the period and there was no control over PM2.5 outside the periphery of the city. A more comprehensive inter-sectoral and inter -state action plan is required to address this alarming issue in this region