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We have used satellite observations and a simulation from the TOMCAT chemistry transport model (CTM) to investigate the influence of the well-known winter-time North Atlantic Oscillation (NAO) on European tropospheric composition. Under the positive phase of the NAO (NAO+), strong westerlies tend to enhance transport of European pollution (e.g. nitrogen oxides, NOx, carbon monoxide, CO) away from anthropogenic source regions. In contrast, during the negative phase of the NAO (NAO−), more stable meteorological conditions lead to a build up of pollutants over these regions, relative to the winter-time average pollution levels. However, the secondary pollutant ozone shows the opposite signal of larger values during NAO+. NAO+ introduces Atlantic ozone-enriched air into Europe while under NAO− westerly transport of ozone is reduced yielding lower values over Europe. Furthermore, ozone concentrations are also decreased by chemical loss through the reaction with accumulated primary pollutants such as nitric oxide (NO) in NAO−. Peroxyacetyl nitrate (PAN), in the upper troposphere-lower stratosphere (UTLS) peaks over Iceland/Southern Greenland in NAO−, between 200–100 hPa, consistent with the trapping by an anticyclone at this altitude. Model simulations show enhanced PAN over Iceland/Southern Greenland in NAO− is associated with vertical transport of polluted air from the troposphere into the UTLS. Overall, this work shows that NAO circulation patterns are an important governing factor for European winter-time composition and air pollution.

Groupings of Lamb weather types (LWTs) are used to assess the influence of synoptic meteorology on UK tropospheric column NO2. Composite satellite maps using daily 2005–2011 data show significant NO2 anomalies for each group. Hence, despite large day-to-day variability and significant uncertainty in individual retrievals, the satellite data can be used to determine national-scale column NO2 variations under different meteorological conditions. Under cyclonic conditions, NO2 is reduced, while anticyclonic conditions aid its accumulation, especially during winter, consistent with trapping of surface emissions and slower photochemical processing. This composite dataset can be used to test seasonal simulations of air quality models.

UK Bonfire Night (BFN) is an annual event on 5 November which celebrates the failed ‘gunpowder plot’ of Guy Fawkes, who intended to blow up the Houses of Parliament. This event is celebrated with firework and bonfire displays, which reduce visibility and increase air pollutant concentrations. A two- to four-fold increase in particulate matter concentrations was seen at some surface monitoring sites. Satellite measurements of aerosol optical depth found increases of 10–90% between days before and after BFN.

The TOMCAT 3-D chemical transport model has been updated with the emissions and chemical degradation of ethene, propene, toluene, butane and monoterpenes. The full tropospheric chemical mechanism is described and the model is evaluated against a range of surface, balloon, aircraft and satellite measurements. The model is generally able to capture the main spatial and seasonal features of high and low concentrations of carbon monoxide (CO), ozone (O3), volatile organic compounds (VOCs) and reactive nitrogen. However, model biases are found, some of which are common to chemistry models and some that are specific to TOMCAT and warrant further investigation. Simulated O3 is found to generally lie within the range of ozonesonde observations and shows good agreement with surface sites. The most notable exceptions to this are during winter at high latitudes, when O3 is underestimated, and during summer over North America, when O3 is overestimated. Global Ozone Monitoring Experiment-2 (GOME-2) comparisons suggest that TOMCAT sub-column tropospheric O3 in DJF may also be underestimated outside of the Arctic, particularly near tropical regions. TOMCAT CO is negatively biased during winter and spring in the Northern Hemisphere (NH) when compared to ground-based observations and MOPITT (Measurements Of Pollution In The Troposphere) satellite data. In contrast, CO is positively biased throughout the year in the Southern Hemisphere (SH). The negative bias in the NH is a common feature in chemistry models and TOMCAT lies well within the range of biases found in other models, while the TOMCAT SH positive bias is at the upper range of positive biases reported in other models. Using two simulations with different boundary conditions highlighted the sensitivity of model performance to the chosen emission dataset when simulating VOCs, nitrogen oxides (NOx) and peroxyacetyl nitrate (PAN). VOC measurements show winter/spring negative biases in C2-C3 alkanes and alkenes, which is likely driven by underestimated anthropogenic emissions. TOMCAT is able to capture the seasonal minima and maxima of PAN and HNO3. However, comparisons to an aircraft climatology show that PAN may be overestimated in winter and HNO3 may be overestimated in winter and spring in regions over North America. The model showed different biases in NOx, depending on location, with evidence of underestimated Asian emissions contributing to negative model biases over China and underestimated fire emissions contributing to negative biases in the SH. TOMCAT global mean tropospheric hydroxyl radical (OH) concentrations are higher than estimates inferred from observations of methyl chloroform, but similar to, or lower than, multi-model mean concentrations reported in recent model intercomparison studies. TOMCAT shows peak OH concentrations in the tropical lower troposphere, unlike other models, which show peak concentrations in the tropical upper troposphere. This is likely to affect the lifetime and transport of important trace gases and warrants further investigation.

Airborne mineral dust is an important component of the Earth system and is increasingly predicted prognostically in weather and climate models. The recent development of data assimilation for remotely sensed aerosol optical depths (AODs) into models offers a new opportunity to better understand the characteristics and sources of model error. Here we examine assimilation increments from Moderate Resolution Imaging Spectroradiometer AODs over northern Africa in the Met Office global forecast model. The model underpredicts (overpredicts) dust in light (strong) winds, consistent with (submesoscale) mesoscale processes lifting dust in reality but being missed by the model. Dust is overpredicted in the Sahara and underpredicted in the Sahel. Using observations of lighting and rain, we show that haboobs (cold pool outflows from moist convection) are an important dust source in reality but are badly handled by the model's convection scheme. The approach shows promise to serve as a useful framework for future model development., Key Points Increments in AOD from data assimilation reveal sources of dust model errorDust underpredicted/overpredicted in light/strong winds and missed from convectionDust overpredicted/underpredicted in Sahara/Sahel

Previous studies have used surface observations of pollutants (e.g., nitrogen dioxide [NO₂] and aerosols) to evaluate improvements in United Kingdom (UK) air quality over recent decades. However, surface monitoring networks provide limited spatial coverage and are not always representative of air quality over a wider region. Satellite observations, such as tropospheric column NO₂ (TCNO₂), aerosol optical depth (AOD) and sub‐column (0–6 km) ozone (SCO₃), provide widespread monitoring of air quality on a national scale. In this study, we use such observations to analyse trends in UK air quality, between 2005 and 2015, finding significant decreases in TCNO₂ and AOD over UK pollution hotspots. The largest changes in short‐lived NO₂ are located over populated (i.e., source) regions, associated with large emissions of nitrogen oxides (NOₓ). AOD changes are more spread out, and less strongly associated with source regions, due to a longer aerosol lifetime, secondary aerosol formation and non‐urban aerosol sources. SCO₃ shows no significant trends over England/Wales, but significant positive trends over Scotland, which is consistent with previous studies of changes in background tropospheric ozone in other remote regions of north‐western Europe.

The search for the perfect "odor (odour, the international English spelling) regulation" most likely began a few thousand years ago in a community that smelled something annoying. That search continues today in societies throughout the world. However, different societies have inherently different levels of acceptance for odors in their communities. Ambient air holds a mixture of chemicals from the everyday activities of industrial and commercial enterprises that make up modern day society. Exposure to those chemicals in the ambient air has become a part of modern day life. However, from time to time, citizens find the odors of these chemicals annoying and objectionable and at some point may declare them a nuisance. Community odors remain one of the top air pollution complaints to regulators and government bodies. An odor nuisance usually is a result of a series of odor episodes experienced by a citizen or citizens. The frequency of these episodes, the duration of each odor episode, the intensity of the odors, and the character or offensiveness of the odors all contribute to the nuisance experience. From state to state, in communities across the United States, and in other countries odor issues are addressed by a variety of "odor laws", whether they are called an ordinance, rule, regulation, or policy. An “odor law” is effective if, and only if; the odor law uses a criterion or criteria to define compliance. The “odor laws” address community odor issues in several well defined approaches that utilize “compliance determining criteria”: annoyance criteria (subjective categories and complaint criteria), ambient odor criteria (threshold or intensity), ambient odorant criteria (mass concentration, i.e. milligram per cubic meter, mg/m), episode duration-frequency criteria, source emission criteria (threshold or mass concentration), and best available control technology criteria (i.e. industry standard). The various approaches are not mutually exclusive and are sometimes combined in one "odor law". Underlying the "approaches" to odor laws are the basic elements that have been placed into successful regulations of air pollutants other than odors. However, these elements must be adapted and formulated for an "odor law". Programs to educate and infrastructure to guide and administer the "odor law" are needed to support the community, whether it is state, county, or local jurisdiction.

The colonization of deciduous leaf litter by aquatic invertebrates was studied at Scott Lake in Algonquin Park, Ontario, Canada. Deciduous leaf packs were colonized after only 2 days submergence. The invertebrate community was dominated by chironomids (25–94% depending on sampling period), and to a lesser extent by oligochaetes, turbellarians, and mayflies. Collectors, such as the chironomids Dicrotendipes, Pseudochironomus, Paratanytarsus and Parakiefferiella were the dominant functional-feeding group suggesting that leaf litter is being used as habitat rather than a direct food source. Deciduous leaf litter lost a substantial amount of weight, due to leaching, after only 48 h submergence. Fall-shed beech (Fagus grandifolia) leaves decomposed more rapidly than fall-shed sugar maple (Acer saccharum) leaves with daily processing coefficients (k), determined using an exponential decay model, of 0.0058 and 0.0039, respectively. Conversely, conditioned maple leaves, defined as leaves remaining on the ground over winter, were processed faster than conditioned beech leaves, with coefficients of 0.0042 and 0.0014, respectively. It is speculated that inhibitory compounds have been leached from the maple leaves, allowing for faster leaf processing.

It takes me 25 minutes to get to work via London's Tube, and about the same time to cycle to work. I prefer to cycle'the Tube is crowded and prone to delays'but there are times when it's too cold or wet to avoid the train. Last year, I realized that making the decision before my daily commute to cycle or take the Tube had become a surprisingly complex affair. Transport for London, which runs the city's transit network, opened up its application programming interfaces (APIs) in 2010 to developers as part of the London Datastore, which aggregates data from a number of public sector organizations serving the city (http://data.london.gov.uk). This led to the creation of dozens of status and travel planning apps for commuters. Consequently, I'd check the weather forecast the evening before and the status of the Tube on my phone in the morning. I'd hooked up an instant messenger alert (via the IFTTT.com service) for rain. None of this was particularly draining, but it was an extra bunch of things that made Monday mornings a bit more Monday morning-ey. I wanted something that would handle all that data and help me spend less time choosing and more time drinking my tea. So I created the Bicycle Barometer, with a needle that swings between a bicycle and a Tube icon depending on the conditions