Climate change effects on airborne pathogenic bioaerosol concentrations: a scenario analysis.

The most recent IPCC report presented further scientific evidence for global climate change in the twenty-first century. Important secondary effects of climate change include those on water resource availability, agricultural yields, urban healthy living, biodiversity, ecosystems, food security, and public health. The aim of this explorative study was to determine the range of expected airborne pathogen concentrations during a single outbreak or release in a future climate compared to a historical climatic period (1981-2010). We used five climate scenarios for the periods 2016-2045 and 2036-2065 defined by the Royal Netherlands Meteorological Institute and two conversion tools to create hourly future meteorological data sets. We modelled season-averaged airborne pathogen concentrations by means of an atmospheric dispersion model and compared these data to historical (1981-2010) modelled concentrations. Our results showed that modelled concentrations were modified several percentage points on average as a result of climate change. On average, concentrations were reduced in four out of five scenarios. Wind speed and global radiation were of critical importance, which determine horizontal and vertical dilution. Modelled concentrations decreased on average, but large positive and negative hourly averaged effects were calculated (from -67 to +639 %). This explorative study shows that further research should include pathogen inactivation and more detailed probability functions on precipitation, snow, and large-scale circulation.

Human Q fever incidence is associated to spatiotemporal environmental conditions.

Airborne pathogenic transmission from sources to humans is characterised by atmospheric dispersion and influence of environmental conditions on deposition and reaerosolisation. We applied a One Health approach using human, veterinary and environmental data regarding the 2009 epidemic in The Netherlands, and investigated whether observed human Q fever incidence rates were correlated to environmental risk factors. We identified 158 putative sources (dairy goat and sheep farms) and included 2339 human cases. We performed a high-resolution (1 × 1 km) zero-inflated regression analysis to predict incidence rates by Coxiella burnetii concentration (using an atmospheric dispersion model and meteorological data), and environmental factors - including vegetation density, soil moisture, soil erosion sensitivity, and land use data - at a yearly and monthly time-resolution. With respect to the annual data, airborne concentration was the most important predictor variable (positively correlated to incidence rate), followed by vegetation density (negatively). The other variables were also important, but to a less extent. High erosion sensitive soils and the land-use fractions "city" and "forest" were positively correlated. Soil moisture and land-use "open nature" were negatively associated. The geographical prediction map identified the largest Q fever outbreak areas. The hazard map identified highest hazards in a livestock dense area. We conclude that environmental conditions are correlated to human Q fever incidence rate. Similar research with data from other outbreaks would be needed to more firmly establish our findings. This could lead to better estimations of the public health risk of a C. burnetii outbreak, and to more detailed and accurate hazard maps that could be used for spatial planning of livestock operations.

Quantitative simulation tools to analyze up- and downstream interactions of soil and water conservation measures: supporting policy making in the Green Water Credits program of Kenya.

Upstream soil and water conservation measures in catchments can have positive impact both upstream in terms of less erosion and higher crop yields, but also downstream by less sediment flow into reservoirs and increased groundwater recharge. Green Water Credits (GWC) schemes are being developed to encourage upstream farmers to invest in soil and water conservation practices which will positively effect upstream and downstream water availability. Quantitative information on water and sediment fluxes is crucial as a basis for such financial schemes. A pilot design project in the large and strategically important Upper-Tana Basin in Kenya has the objective to develop a methodological framework for this purpose. The essence of the methodology is the integration and use of a collection of public domain tools and datasets: the so-called Green water and Blue water Assessment Toolkit (GBAT). This toolkit was applied in order to study different options to implement GWC in agricultural rainfed land for the pilot study. Impact of vegetative contour strips, mulching, and tied ridges were determined for: (i) three upstream key indicators: soil loss, crop transpiration and soil evaporation, and (ii) two downstream indicators: sediment inflow in reservoirs and groundwater recharge. All effects were compared with a baseline scenario of average conditions. Thus, not only actual land management was considered but also potential benefits of changed land use practices. Results of the simulations indicate that especially applying contour strips or tied ridges significantly reduces soil losses and increases groundwater recharge in the catchment. The model was used to build spatial expressions of the proposed management practices in order to assess their effectiveness. The developed procedure allows exploring the effects of soil conservation measures in a catchment to support the implementation of GWC.