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The Clean Air for London (ClearfLo) project provides integrated measurements of the meteorology, composition, and particulate loading of the urban atmosphere in London, United Kingdom, to improve predictive capability for air quality. METEOROLOGY, AIR QUALITY, AND HEALTH IN LONDONThe ClearfLo Project Economic and Social Affairs 2013). Urban populations are exposed to stressful environmental conditions, such as local and nonlocal pollutants, that cause poor air quality and microclimates that exacerbate heat stress during heat waves. These are projected to increase in a warming climate. Our cities are therefore nexus points for several environmental health stresses that we currently face (Rydin et al. 2012) and the interacting issues around sustainability and human health.The purpose of this paper is to introduce the Clean Air for London (ClearfLo) project, which investigates the atmospheric science that underpins these health stresses, with a particular focus on the urban increment in atmospheric drivers. We focused on three atmospheric drivers of environmental health stress in cities, namely, heat, gas-phase pollutants, and particulate matter (PM). Health stresses from the urban atmospheric environment.Heat waves have an impact on human health. Populations typically display an optimal temperature range at which the (daily or weekly) mortality rate is lowest. Mortality rates rise as temperatures exceed this optimal range (e.g., Rydin et al. 2012). The 2003 European heat wave (Stedman 2004) in combination with air pollution was responsible for more than 2000 excess deaths in the United Kingdom (Johnson et al. 2005). Under a warming climate, the risks posed by heat stress are predicted to increase (Hacker et al. 2005). People living in urban environments are exposed to higher temperatures than in nonurban regions. Thus, heat-related deaths could be higher within urban areas (Mavrogianni et al. 2011). Hence, ClearfLo is concerned with measuring the factors controlling the urban atmospheric boundary layer, that is, the surface energy balance.The World Health Organization (WHO) reported (WHO 2006) that the strongest effects of air quality 779MAY 2015 AMERICAN METEOROLOGICAL SOCIETY | on health are attributable to PM, followed by ozone (O 3 ) and nitrogen dioxide (NO 2 ). A recent report (Guerreiro et al. 2013) indicates that in 2011 up to 88% of the urban population in Europe was exposed to concentrations exceeding the WHO air quality guidelines for PM 10 (defined as particles that pass through a size-selective inlet with a 50% efficiency cutoff at 10-µm aerodynamic diameter, representative of the inhalable fraction). It is estimated that a reduction of PM 10 to the WHO annual-mean guideline of 20 µg m −3 would reduce attributable deaths per year in Europe by 22,000. Further, this would lead to a substantial improvement in the quality of life for millions with a preexisting respiratory or cardiovascular disease (COMEAP 2010).Epidemiological studies consistently demonstrate an association between the PM mass concentr...

All eddy-covariance flux measurements are associated with random uncertainties which are a combination of sampling error due to natural variability in turbulence and sensor noise. The former is the principal error for systems where the signal-to-noise ratio of the analyser is high, as is usually the case when measuring fluxes of heat, CO 2 or H 2 O. Where signal is limited, which is often the case for measurements of other trace gases and aerosols, instrument uncertainties dominate. Here, we are applying a consistent approach based on auto-and cross-covariance functions to quantify the total random flux error and the random error due to instrument noise separately. As with previous approaches, the random error quantification assumes that the time lag between wind and concentration measurement is known. However, if combined with commonly used automated methods that identify the individual time lag by looking for the maximum in the cross-covariance function of the two entities, analyser noise additionally leads to a systematic bias in the fluxes. Combining data sets from several analysers and using simulations, we show that the method of time-lag determination becomes increasingly important as the magnitude of the instrument error approaches that of the sampling error. The flux bias can be particularly significant for disjunct data, whereas using a prescribed time lag eliminates these effects (provided the time lag does not fluctuate unduly over time). We also demonstrate that when sampling at higher elevations, where low frequency turbulence dominates and covariance peaks are broader, both the probability and magnitude of bias are magnified. We show that the statistical significance of noisy flux data can be increased (limit of detection can be decreased) by appropriate averaging of individual fluxes, but only if systematic biases are avoided by using a prescribed time lag. Finally, we make recommendations for the analysis and reporting of data with low signal-to-noise and their associated errors.Published by Copernicus Publications on behalf of the European Geosciences Union.

We test the relationship between canopy photosynthesis and reflected near‐infrared radiation from vegetation across a range of functional (photosynthetic pathway and capacity) and structural conditions (leaf area index, fraction of green and dead leaves, canopy height, reproductive stage, and leaf angle inclination), weather conditions, and years using a network of field sites from across central California. We based our analysis on direct measurements of canopy photosynthesis, with eddy covariance, and measurements of reflected near‐infrared and red radiation from vegetation, with light‐emitting diode sensors. And we interpreted the observed relationships between photosynthesis and reflected near‐infrared radiation using simulations based on the multilayer, biophysical model, CanVeg. Measurements of reflected near‐infrared radiation were highly correlated with measurements of canopy photosynthesis on half‐hourly, daily, seasonal, annual, and decadal time scales across the wide range of function and structure and weather conditions. Slopes of the regression between canopy photosynthesis and reflected near‐infrared radiation were greatest for the fertilized and irrigated C4 corn crop, intermediate for the C3 tules on nutrient‐rich organic soil and nitrogen fixing alfalfa, and least for the native annual grasslands and oak savanna on nutrient‐poor, mineral soils. Reflected near‐infrared radiation from vegetation has several advantages over other remotely sensed vegetation indices that are used to infer canopy photosynthesis; it does not saturate at high leaf area indices, it is insensitive to the presence of dead legacy vegetation, the sensors are inexpensive, and the reflectance signal is strong. Hence, information on reflected near‐infrared radiation from vegetation may have utility in monitoring carbon assimilation in carbon sequestration projects or on microsatellites orbiting Earth for precision agriculture applications.