Local spatiotemporal dynamics of particulate matter and oak pollen measured by machine learning aided optical particle counters.

Conventional techniques for monitoring pollen currently have significant limitations in terms of labour, cost and the spatiotemporal resolution that can be achieved. Pollen monitoring networks across the world are generally sparse and are not able to fully represent the detailed characteristics of airborne pollen. There are few studies that observe concentrations on a local scale, and even fewer that do so in ecologically rich rural areas and close to emitting sources. Better understanding of these would be relevant to occupational risk assessments for public health, as well as ecology, biodiversity, and climate. We present a study using low-cost optical particle counters (OPCs) and the application of machine learning models to monitor particulate matter and pollen within a mature oak forest in the UK. We characterise the observed oak pollen concentrations, first during an OPC colocation period (6 days) for calibration purposes, then for a period (36 days) when the OPCs were distributed on an observational tower at different heights through the canopy. We assess the efficacy and usefulness of this method and discuss directions for future development, including the requirements for training data. The results show promise, with the derived pollen concentrations following the expected diurnal trends and interactions with meteorological variables. Quercus pollen concentrations appeared greatest when measured at the canopy height of the forest (20-30 m). Quercus pollen concentrations were lowest at the greatest measurement height that is above the canopy (40 m), which is congruent with previous studies of background pollen in urban environments. The attenuation of pollen concentrations as sources are depleted is also observed across the season and at different heights, with some evidence that the pollen concentrations persist later at the lowest level beneath the canopy (10 m) where catkins mature latest in the season compared to higher catkins.

Optimal stomatal theory predicts CO2 responses of stomatal conductance in both gymnosperm and angiosperm trees.

Optimal stomatal theory predicts that stomata operate to maximise photosynthesis (Anet ) and minimise transpirational water loss to achieve optimal intrinsic water-use efficiency (iWUE). We tested whether this theory can predict stomatal responses to elevated atmospheric CO2 (eCO2 ), and whether it can capture differences in responsiveness among woody plant functional types (PFTs). We conducted a meta-analysis of tree studies of the effect of eCO2 on iWUE and its components Anet and stomatal conductance (gs ). We compared three PFTs, using the unified stomatal optimisation (USO) model to account for confounding effects of leaf-air vapour pressure difference (D). We expected smaller gs , but greater Anet , responses to eCO2 in gymnosperms compared with angiosperm PFTs. We found that iWUE increased in proportion to increasing eCO2 in all PFTs, and that increases in Anet had stronger effects than reductions in gs . The USO model correctly captured stomatal behaviour with eCO2 across most datasets. The chief difference among PFTs was a lower stomatal slope parameter (g1 ) for the gymnosperm, compared with angiosperm, species. Land surface models can use the USO model to describe stomatal behaviour under changing atmospheric CO2 conditions.

Characteristics of free air carbon dioxide enrichment of a northern temperate mature forest.

In 2017, the Birmingham Institute of Forest Research (BIFoR) began to conduct Free Air Carbon Dioxide Enrichment (FACE) within a mature broadleaf deciduous forest situated in the United Kingdom. BIFoR FACE employs large-scale infrastructure, in the form of lattice towers, forming 'arrays' which encircle a forest plot of ~30 m diameter. BIFoR FACE consists of three treatment arrays to elevate local CO2 concentrations (e[CO2 ]) by +150 µmol/mol. In practice, acceptable operational enrichment (ambient [CO2 ] + e[CO2 ]) is ±20% of the set point 1-min average target. There are a further three arrays that replicate the infrastructure and deliver ambient air as paired controls for the treatment arrays. For the first growing season with e[CO2 ] (April to November 2017), [CO2 ] measurements in treatment and control arrays show that the target concentration was successfully delivered, that is: +147 ± 21 µmol/mol (mean ± SD) or 98 ± 14% of set point enrichment target. e[CO2 ] treatment was accomplished for 97.7% of the scheduled operation time, with the remaining time lost due to engineering faults (0.6% of the time), CO2 supply issues (0.6%) or adverse weather conditions (1.1%). CO2 demand in the facility was driven predominantly by wind speed and the formation of the deciduous canopy. Deviations greater than 10% from the ambient baseline CO2 occurred <1% of the time in control arrays. Incidences of cross-contamination >80 µmol/mol (i.e. >53% of the treatment increment) into control arrays accounted for <0.1% of the enrichment period. The median [CO2 ] values in reconstructed three-dimensional [CO2 ] fields show enrichment somewhat lower than the target but still well above ambient. The data presented here provide confidence in the facility setup and can be used to guide future next-generation forest FACE facilities built into tall and complex forest stands.

Effectiveness of green infrastructure for improvement of air quality in urban street canyons.

Street-level concentrations of nitrogen dioxide (NO(2)) and particulate matter (PM) exceed public health standards in many cities, causing increased mortality and morbidity. Concentrations can be reduced by controlling emissions, increasing dispersion, or increasing deposition rates, but little attention has been paid to the latter as a pollution control method. Both NO(2) and PM are deposited onto surfaces at rates that vary according to the nature of the surface; deposition rates to vegetation are much higher than those to hard, built surfaces. Previously, city-scale studies have suggested that deposition to vegetation can make a very modest improvement (<5%) to urban air quality. However, few studies take full account of the interplay between urban form and vegetation, specifically the enhanced residence time of air in street canyons. This study shows that increasing deposition by the planting of vegetation in street canyons can reduce street-level concentrations in those canyons by as much as 40% for NO(2) and 60% for PM. Substantial street-level air quality improvements can be gained through action at the scale of a single street canyon or across city-sized areas of canyons. Moreover, vegetation will continue to offer benefits in the reduction of pollution even if the traffic source is removed from city centers. Thus, judicious use of vegetation can create an efficient urban pollutant filter, yielding rapid and sustained improvements in street-level air quality in dense urban areas.

Development and application of an urban tree air quality score for photochemical pollution episodes using the Birmingham, United Kingdom, area as a case study.

An atmospheric chemistry model (CiTTyCAT) is used to quantify the effects of trees on urban air quality in scenarios of high photochemical pollution. The combined effects of both pollutant deposition to and emission of biogenic volatile organic compounds (BVOC) from the urban forest are considered, and the West Midlands, metropolitan area in the UK is used as a case study. While all trees can be beneficial to air quality in terms of the deposition of O3, NO2, CO, and HNO3, some trees have the potential to contribute to the formation of O3 due to the reaction of BVOC and NOx. A number of model scenarios are used to develop an urban tree air quality score (UTAQS) that ranks trees in order of their potential to improve air quality. Of the 30 species considered, pine, larch, and silver birch have the greatest potential to improve urban air quality, while oaks, willows, and poplars can worsen downwind air quality if planted in very large numbers. The UTAQS classification is designed with practitioners in mind, to help them achieve sustainable urban air quality. The UTAQS classification is applicable to all urban areas of the UK and other mid-latitude, temperate climate zones that have tree species common to those found in UK urban areas. The modeling approach used here is directly applicable to all areas of the world given the appropriate input data. It provides a tool that can help to achieve future sustainable urban air quality.