E.J. Carnell

Nutrient fluxes from domestic wastewater: A national-scale historical perspective for the UK 1800-2010.

Nutrient emissions in human waste and wastewater effluent fluxes from domestic sources are quantified for the UK over the period 1800-2010 based on population data from UK Census returns. The most important drivers of change have been the introduction of the water closet (flush toilet) along with population growth, urbanization, connection to sewer, improvements in wastewater treatment and use of phosphorus in detergents. In 1800, the population of the UK was about 12 million and estimated emissions in human waste were 37kt N, 6.2kt P and 205ktorganicC/year. This would have been recycled to land with little or no sewage going directly to rivers or coastal waters. By 1900, population had increased to 35.6 million and some 145kt N were emitted in human waste but, with only the major urban areas connected to sewers, only about 19kt N were discharged in sewage effluent. With the use of phosphorus in detergents, estimated phosphorus emissions peaked at around 63.5ktP/year in the 1980s, with about 28ktP/year being discharged in sewage effluent. By 2010, population had increased to 63 million with estimated emissions of 263kt N, 43.6kt P and 1460ktorganicC/year, and an estimated effluent flux of 104kt N, 14.8kt P and 63kt organic C. Despite improvements in wastewater treatment, current levels of nutrient fluxes in sewage effluent are substantially higher than those in the early 20th century.

Long-term increases in soil carbon due to ecosystem fertilization by atmospheric nitrogen deposition demonstrated by regional-scale modelling and observations

Fertilization of nitrogen (N)-limited ecosystems by anthropogenic atmospheric nitrogen deposition (Ndep) may promote CO2 removal from the atmosphere, thereby buffering human effects on global radiative forcing. We used the biogeochemical ecosystem model N14CP, which considers interactions among C (carbon), N and P (phosphorus), driven by a new reconstruction of historical Ndep, to assess the responses of soil organic carbon (SOC) stocks in British semi-natural landscapes to anthropogenic change. We calculate that increased net primary production due to Ndep has enhanced detrital inputs of C to soils, causing an average increase of 1.2 kgCm−2 (c. 10%) in soil SOC over the period 1750–2010. The simulation results are consistent with observed changes in topsoil SOC concentration in the late 20th Century, derived from sample-resample measurements at nearly 2000 field sites. More than half (57%) of the additional topsoil SOC is predicted to have a short turnover time (c. 20 years), and will therefore be sensitive to future changes in Ndep. The results are the first to validate model predictions of Ndep effects against observations of SOC at a regional field scale. They demonstrate the importance of long-term macronutrient interactions and the transitory nature of soil responses in the terrestrial C cycle.

Impact of two centuries of intensive agriculture on soil carbon, nitrogen and phosphorus cycling in the UK

This paper describes an agricultural model (Roth-CNP) that estimates carbon (C), nitrogen (N) and phosphorus (P) pools, pool changes, their balance and the nutrient fluxes exported from arable and grassland systems in the UK during 1800–2010. The Roth-CNP model was developed as part of an Integrated Model (IM) to simulate C, N and P cycling for the whole of UK, by loosely coupling terrestrial, hydrological and hydro-chemical models. The model was calibrated and tested using long term experiment (LTE) data from Broadbalk (1843) and Park Grass (1856) at Rothamsted. We estimated C, N and P balance and their fluxes exported from arable and grassland systems on a 5 km × 5 km grid across the whole of UK by using the area of arable of crops and livestock numbers in each grid and their management. The model estimated crop and grass yields, soil organic carbon (SOC) stocks and nutrient fluxes in the form of NH4-N, NO3-N and PO4-P. The simulated crop yields were compared to that reported by national agricultural statistics for the historical to the current period. Overall, arable land in the UK have lost SOC by −0.18, −0.25 and −0.08 Mg C ha−1 y−1 whereas land under improved grassland SOC stock has increased by 0.20, 0.47 and 0.24 Mg C ha−1 y−1 during 1800–1950, 1950–1970 and 1970–2010 simulated in this study. Simulated N loss (by leaching, runoff, soil erosion and denitrification) increased both under arable (−15, −18 and −53 kg N ha−1 y−1) and grass (−18, −22 and −36 kg N ha−1 y−1) during different time periods. Simulated P surplus increased from 2.6, 10.8 and 18.1 kg P ha−1 y−1 under arable and 2.8, 11.3 and 3.6 kg P ha−1 y−1 under grass lands 1800–1950, 1950–1970 and 1970–2010.

The influence of residential and workday population mobility on exposure to air pollution in the UK

Traditional approaches of quantifying population-level exposure to air pollution assume that concentrations of air pollutants at the residential address of the study population are representative for overall exposure. This introduces potential bias in the quantification of human health effects. Our study combines new UK Census data comprising information on workday population densities, with high spatio-temporal resolution air pollution concentration fields from the WRF-EMEP4UK atmospheric chemistry transport model, to derive more realistic estimates of population exposure to NO2, PM2.5 and O3. We explicitly allocated workday exposures for weekdays between 8:00 am and 6:00 pm. Our analyses covered all of the UK at 1 km spatial resolution. Taking workday location into account had the most pronounced impact on potential exposure to NO2, with an estimated 0.3 μg m-3 (equivalent to 2%) increase in population-weighted annual exposure to NO2 across the whole UK population. Population-weighted exposure to PM2.5 and O3 increased and decreased by 0.3%, respectively, reflecting the different atmospheric processes contributing to the spatio-temporal distributions of these pollutants. We also illustrate how our modelling approach can be utilised to quantify individual-level exposure variations due to modelled time-activity patterns for a number of virtual individuals living and working in different locations in three example cities. Changes in annual-mean estimates of NO2 exposure for these individuals were considerably higher than for the total UK population average when including their workday location. Conducting model-based evaluations as described here may contribute to improving representativeness in studies that use small, portable, automatic sensors to estimate personal exposure to air pollution.

Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol

Introduction Asthma has a considerable, but potentially, avoidable burden on many populations globally. Scotland has some of the poorest health outcomes from asthma. Although ambient pollution, weather changes and sociodemographic factors have been associated with asthma attacks, it remains unclear whether modelled environment data and geospatial information can improve population-based asthma predictive algorithms. We aim to create the afferent loop of a national learning health system for asthma in Scotland. We will investigate the associations between ambient pollution, meteorological, geospatial and sociodemographic factors and asthma attacks. Methods and Analysis We will develop and implement a secured data governance and linkage framework to incorporate primary care health data, modelled environment data, geospatial population and sociodemographic data. Data from 75 recruited primary care practices (n=500 000 patients) in Scotland will be used. Modelled environment data on key air pollutants at a horizontal resolution of 5 km×5 km at hourly time steps will be generated using the EMEP4UK atmospheric chemistry transport modelling system for the datazones of the primary care practices’ populations. Scottish population census and education databases will be incorporated into the linkage framework for analysis. We will then undertake a longitudinal retrospective observational analysis. Asthma outcomes include asthma hospitalisations and oral steroid prescriptions. Using a nested case–control study design, associations between all covariates will be measured using conditional logistic regression to account for the matched design and to identify suitable predictors and potential candidate algorithms for an asthma learning health system in Scotland. Findings from this study will contribute to the development of predictive algorithms for asthma outcomes and be used to form the basis for our learning health system prototype. Ethics and dissemination The study received National Health Service Research Ethics Committee approval (16/SS/0130) and also obtained permissions via the Public Benefit and Privacy Panel for Health and Social Care in Scotland to access, collate and use the following data sets: population and housing census for Scotland; Scottish education data via the Scottish Exchange of Data and primary care data from general practice Data Custodians. Analytic code will be made available in the open source GitHub website. The results of this study will be published in international peer reviewed journals.