%0 Journal Article %J Environmental Research Letters %D 2016 %T The UK particulate matter air pollution episode of March-April 2014: more than Saharan dust %A Vieno, M %A Heal, M %A Twigg, M %A MacKenzie, I %A Braban, C %A Lingard, J %A Ritchie, S %A Beck, R %A Móring, A %A Ots, R %A Di Marco, C %A Nemitz, E %A Sutton, M. A. %A Reis, S %X

A period of elevated surface concentrations of airborne particulate matter (PM) in the UK in spring 2014 was widely associated in the UK media with a Saharan dust plume. This might have led to over-emphasis on a natural phenomenon and consequently to a missed opportunity to inform the public and provide robust evidence for policy-makers about the observed characteristics and causes of this pollution event. In this work, the EMEP4UK regional atmospheric chemistry transport model (ACTM) was used in conjunction with speciated PM measurements to investigate the sources and long-range transport (including vertical) processes contributing to the chemical components of the elevated surface PM. It is shown that the elevated PM during this period was mainly driven by ammonium nitrate, much of which was derived from emissions outside the UK. In the early part of the episode, Saharan dust remained aloft above the UK; we show that a significant contribution of Saharan dust at surface level was restricted only to the latter part of the elevated PM period and to a relatively small geographic area in the southern part of the UK. The analyses presented in this paper illustrate the capability of advanced ACTMs, corroborated with chemically-speciated measurements, to identify the underlying causes of complex PM air pollution episodes. Specifically, the analyses highlight the substantial contribution of secondary inorganic ammonium nitrate PM, with agricultural ammonia emissions in continental Europe presenting a major driver. The findings suggest that more emphasis on reducing emissions in Europe would have marked benefits in reducing episodic PM2.5 concentrations in the UK.

%B Environmental Research Letters %V 11 %8 04/2016 %G eng %U http://dx.doi.org/10.1088/1748-9326/11/4/044004 %& 4 %R 10.1088/1748-9326/11/4/044004 %0 Journal Article %J Environmental Modelling & Software %D 2015 %T Integrating modelling and smart sensors for environmental and human health %A Reis, S %A Seto, E %A Northcross, A %A Quinn, N. W. %A Convertino, M %A Jones, R. L. %A Maier, H. R. %A Schlink, U %A Steinle, S %A Vieno, M %A Wimberly, M. C. %X

Sensors are becoming ubiquitous in everyday life, generating data at an unprecedented rate and scale. However, models that assess impacts of human activities on environmental and human health, have typically been developed in contexts where data scarcity is the norm. Models are essential tools to understand processes, identify relationships, associations and causality, formalize stakeholder mental models, and to quantify the effects of prevention and interventions. They can help to explain data, as well as inform the deployment and location of sensors by identifying hotspots and areas of interest where data collection may achieve the best results. We identify a paradigm shift in how the integration of models and sensors can contribute to harnessing ‘Big Data’ and, more importantly, make the vital step from ‘Big Data’ to ‘Big Information’. In this paper, we illustrate current developments and identify key research needs using human and environmental health challenges as an example.

%B Environmental Modelling & Software %V 74 %8 12/2015 %G eng %& 238 %R doi:10.1016/j.envsoft.2015.06.003