Abstract. Ceilometer lidars are used for cloud base height detection, to probe aerosol layers in the atmosphere (e.g. detection of elevated layers of Saharan dust or volcanic ash), and to examine boundary layer dynamics. Sensor optics and acquisition algorithms can strongly influence the observed attenuated backscatter profiles; therefore, physical interpretation of the profiles requires careful application of corrections. This study addresses the widely deployed Vaisala CL31 ceilometer. Attenuated backscatter profiles are studied to evaluate the impact of both the hardware generation and firmware version. In response to this work and discussion within the CL31/TOPROF user community (TOPROF, European COST Action aiming to harmonise ground-based remote sensing networks across Europe), Vaisala released new firmware (versions 1.72 and 2.03) for the CL31 sensors. These firmware versions are tested against previous versions, showing that several artificial features introduced by the data processing have been removed. Hence, it is recommended to use this recent firmware for analysing attenuated backscatter profiles. To allow for consistent processing of historic data, correction procedures have been developed that account for artefacts detected in data collected with older firmware. Furthermore, a procedure is proposed to determine and account for the instrument-related background signal from electronic and optical components. This is necessary for using attenuated backscatter observations from any CL31 ceilometer. Recommendations are made for the processing of attenuated backscatter observed with Vaisala CL31 sensors, including the estimation of noise which is not provided in the standard CL31 output. After taking these aspects into account, attenuated backscatter profiles from Vaisala CL31 ceilometers are considered capable of providing valuable information for a range of applications including atmospheric boundary layer studies, detection of elevated aerosol layers, and model verification.

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The use of Automatic Lidars and Ceilometers (ALC) is increasingly extended beyond monitoring cloud base height to the study of atmospheric boundary layer (ABL) dynamics. Therefore, long-term sensor network observations require robust algorithms to automatically detect the mixed layer height (Z ML ). Here, a novel automatic algorithm CABAM (Characterising the Atmospheric Boundary layer based on ALC Measurements) is presented. CABAM is the first non-proprietary mixed layer height algorithm specifically designed for the commonly deployed Vaisala CL31 ceilometer. The method tracks Z ML , takes into account precipitation, classifies the ABL based on cloud cover and cloud type, and determines the relation between Z ML and cloud base height. CABAM relies solely on ALC measurements. Results perform well against independent reference (AMDAR: Aircraft Meteorological Data Relay) measurements and supervised Z ML detection. AMDAR-derived temperature inversion heights allow Z ML evaluation throughout the day. Very good agreement is found in the afternoon when the mixed layer height extends over the full ABL. However, during night or the morning transition the temperature inversion is more likely associated with the top of the residual layer. From comparison with SYNOP reports, the ABL classification scheme generally correctly distinguishes between convective and stratiform boundary-layer clouds, with slightly better performance during daytime. Applied to 6 years of ALC observations in central London, Kotthaus and Grimmond (2018), a companion paper, demonstrate CABAM results are valuable to characterise the urban boundary layer over London, United Kingdom, where clouds of various types are frequent. KEYWORDSABL, ALC, AMDAR, boundary-layer clouds, CABAM, ceilometer, mixed layer height Abbreviations: ABL, atmospheric boundary layer; agl, above ground level; ALC, automatic lidars and ceilometers; AMDAR, Aircraft Meteorological Data Relay; AN, afternoon (mid-point between solar noon and sunset); B, end time of layer; CABAM, Characterising the Atmospheric Boundary layer based on ALC Measurements; CBH, cloud base height; CC, cloud cover; Cu, cumulus cloud, also ABL class of days dominated by Cu; d, prefix, indicating a difference; D, second derivative of attenuated backscatter; day SR , 24 hr periods centred on sunrise; ET, evening transition; g, range gate index; G, vertical gradient of attenuated backscatter; H day , duration of day; H night , duration of night; i, index of iteration; IQR, inter-quartile range; K, layer detection density; L, layer duration; MBE, mean bias error; MH± 1 , mixed layer height at end of previous day/beginning of next day; ML, mixed layer; MN, midnight; MT, morning transition; N, number of layers; n, number of layers fulfilling certain criteria; NT, nocturnal layer after sunset; P, number of points forming a layer; p, percentile; q, index of layer; r, range (distance from instrument); R, size of range window; RL, residual layer; RLH, height of residual layer; RMSE, root-mean-square error; SC, stratocumul...

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...

Abstract. Black carbon (BC) is known to have major impacts on both human health and climate. The populated megacity represents the most complex anthropogenic BC emissions where the sources and related impacts are very uncertain. This study provides source attribution and characterization of BC in the Beijing urban environment during the joint UK–China APHH (Air Pollution and Human Health) project, in both winter (November–December 2016) and summer (May–June 2017). The size-resolved mixing state of BC-containing particles was characterized by a single-particle soot photometer (SP2) and their mass spectra was measured by a soot particle aerosol mass spectrometer (SP-AMS). The refractory BC (rBC) mass loading was around a factor of 2 higher in winter relative to summer, and more variable coatings were present, likely as a result of additional surface emissions from the residential sector and favourable condensation in the cold season. The characteristics of the BC were relatively independent of air mass direction in summer, whereas in winter air masses from the Northern Plateau were considerably cleaner and contained less-coated and smaller BC, but the BC from the Southern Plateau had the largest core size and coatings. We compare two online source apportionment methods using simultaneous measurements made by the SP2, which measures physical properties of BC, and the chemical approach using the positive matrix factorization (PMF) of mass spectra from the SP-AMS for the first time. A method is proposed to isolate the BC from the transportation sector using a mode of small BC particles (core diameter Dc<0.18 µm and coating thickness ct < 50 nm). This mode of BC highly correlated with NOx concentration in both seasons (∼14 ng m−3 BC ppb−1 NOx) and corresponded with the morning traffic rush hour, contributing about 30 % and 40 % of the total rBC mass (35 % and 55 % in number) in winter and summer respectively. The BC from coal burning or biomass burning was characterized by moderate coatings (ct = 50–200 nm) contributing ∼20 %–25 % of rBC mass. Large uncoated BC particles (Dc>0.18 µm and ct < 50 nm) were more likely to be contributed by coal combustion, as these particles were not present in urban London. This mode was present in Beijing in both winter (∼30 %–40 % rBC mass) and summer (∼40 % rBC mass) but may be dominated by the residential and industrial sector respectively. The contribution of BC thickly coated with secondary species (ct > 200 nm) to the total rBC mass increased with pollution level in winter but was minor in summer. These large BC particles importantly enhanced the absorption efficiency at high pollution levels – in winter when PM1 > 100 µg m−3 or BC > 2 µg m−3, the absorption efficiency of BC increased by 25 %–70 %. The reduction of emissions of these large BC particles and the precursors of the associated secondary coating will be an effective way of mitigating the heating effect of BC in urban environments.

Abstract. The Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-Beijing) programme is an international collaborative project focusing on understanding the sources, processes and health effects of air pollution in the Beijing megacity. APHH-Beijing brings together leading China and UK research groups, state-of-the-art infrastructure and air quality models to work on four research themes: (1) sources and emissions of air pollutants; (2) atmospheric processes affecting urban air pollution; (3) air pollution exposure and health impacts; and (4) interventions and solutions. Themes 1 and 2 are closely integrated and support Theme 3, while Themes 1–3 provide scientific data for Theme 4 to develop cost-effective air pollution mitigation solutions. This paper provides an introduction to (i) the rationale of the APHH-Beijing programme and (ii) the measurement and modelling activities performed as part of it. In addition, this paper introduces the meteorology and air quality conditions during two joint intensive field campaigns – a core integration activity in APHH-Beijing. The coordinated campaigns provided observations of the atmospheric chemistry and physics at two sites: (i) the Institute of Atmospheric Physics in central Beijing and (ii) Pinggu in rural Beijing during 10 November–10 December 2016 (winter) and 21 May–22 June 2017 (summer). The campaigns were complemented by numerical modelling and automatic air quality and low-cost sensor observations in the Beijing megacity. In summary, the paper provides background information on the APHH-Beijing programme and sets the scene for more focused papers addressing specific aspects, processes and effects of air pollution in Beijing.

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