Tropical Climate

The selected study sites in the Maprik and Wosera-Gawi districts cover a combined area of over 160 km² that is characterized by low hills, plains and riverine plains with a wet tropical climate [26 (link)]. The natural vegetation is lowland hill forest that has mostly been replaced by re-growth following cultivation. Extensive grasslands are common on the riverine plans near the Sepik River. The Wosera study site described previously [26 (link)] is situated near the center of this expanded study region. In the early 1990's malaria was found to be holoendemic with a peak prevalence of 77% in children 5–9 years of age in the central Wosera area [26 (link)], with entomological inoculation rates (EIR) estimated at 35.4 infective bites/person-year for P. falciparum, 12.1 for P. vivax, and 9.6 for P. malariae [27 (link)]. By comparison, recent human studies in the Wosera found prevalence of P. falciparum to be significantly reduced [24 (link)].
For the present study five distinct areas surrounding local health facilities (i.e. Brukham, Burui, Ilaita, Ulupu and Wombisa HC) were selected (Figure 1). Brukham, Ilaita and Ulupu are situated in the foothills of the Prince Alexander range, Burui and Wombisa are in the Sepik River plain. In April and May 2005, 3 villages were surveyed in each of the five areas. In order to achieve a near-random sample of the village population a household-based sampling strategy was pursued. A number of households with a total population of 150–200 persons were included in each survey. The surveys included every member of the selected households who could be reached on the day of survey. Individual informed consent was obtained from all study participants (or consent granted by parents or guardians for inclusion of children) using study protocols evaluated and approved by the PNG Medical Research Advisory Committee (MRAC 00.26 & MRAC 05.20).
From each household a semi-structured questionnaire was administered to collect data on type of house, household assets, education of the parents, personal use of bednets, recent health facility attendance and use of antimalarial drugs. From each individual, a thick and thin film was prepared on a single slide and a 250 μl blood sample collected into a K+EDTA microtainer from a finger or heel prick. Haemoglobin measurements were made from this blood sample using a HemoCue 201+ Hb meter (Angholm, Sweden); remaining blood was preserved for extraction of DNA.
A socio-economic index was created using data on household ownership of consumer durables (i.e. bed, mattress, bednet, chairs, umbrella, clothing cupboard, kerosene pressure lamp, kerosene cooker, electric torch, radio, television, car). Households that owned < five consumer durables were classified as "low", > five consumer durables were classified as "medium", those with > 10 as "high" socio-economic status.

The MFT of each location was obtained by calculating the mode of daily mean temperature distributed in the 54^th–92^th range during the entire year, which is the 95% distribution of the MMPs. We systematically analysed the distribution of the MMTs, annual mean temperature, 78^th percentile temperature and MFTs in 420 locations from 30 countries. We compared the two existing indicators (annual mean temperature and 78^th percentile temperature) and the new indicator (MFT).
We applied a MLR model to fit the MMT according to the seven independent variables. The model is defined below (Equation (1)): $y=\alpha +\sum _{i=1}^7{\beta }_i{x}_i$ where y is the MMT, α is the intercept. x₁,…x_i (i = 1,…,7) are the independent variables in Table 1, and β₁,…β_i (i = 1,…, 7) are the regression coefficients.
We mapped the error (Equation (2)) histograms of the above three temperature indexes and calculated the Pearson correlations among them and the MMT. $Error=∣T-MMT∣$ where T refers to the three temperatures mentioned above.
The 420 locations in the present study covered eight of the 11 major climatic zones, (including tropical grassland, tropical monsoon, tropical rainforest, subtropical monsoon, temperate continental, temperate maritime, temperate monsoon, and Mediterranean climates, excluding for the tropical desert, plateau mountain, and cold climates). We combined eight climatic zones into three categories: temperate climate (including 91 locations), subtropical climate (including 174 locations), and tropical climate (including 155 locations) regions. For these three main climate regions, we analysed the associations between the MFT, the 78th percentile temperature, the annual mean temperature, and the MMT (Fig. 3).
With the projected daily mean temperature under five different global-scale general circulation models (GCMs) and two Representative Concentration Pathways scenarios (RCP4.5 and RCP8.5), we estimate the global distribution of MMTs in the present (2010s) and future (2050s) for each 0.5° × 0.5° grid (Figs. 4 and  5).

The Integrated Environmental Modeller (IEM) [13 , 23 –25 ] has been developed as an integrated multi-physics urban microclimatic modeling tool for urban environment evaluation customized for the tropical climate. IEM can simulate the coupling between solar irradiance, wind flow, air temperature, and traffic noise propagation. These physical models in IEM have been validated for solar radiation [13 ], wind dynamics [23 , 24 ], and traffic-noise simulations [25 ] under the climate conditions in Singapore. Fig 3 shows the workflow of IEM. Two different methods are available for calculating solar irradiance: ray-tracing and discrete ordinates models. The ray-tracing approach was used for standalone solar irradiance studies, while the discrete ordinate models were used for wind-thermal computations to keep the computational cost low. Furthermore, the Perez all-weather sky model [13 , 43 ] was incorporated as it has the best overall performance over a wide range of locations [44 ] and is one of the most suitable transposition models to predict solar irradiance for Singapore [45 , 46 ]. Using the solar heat solver in IEM, the solar irradiance values on all surfaces in the studied system can be generated and passed to the aerodynamic simulation as input parameters.
The aerodynamic model in IEM was developed within OpenFOAM^®, a finite-volume computational fluid dynamics (CFD) platform. Buoyancy effects were modeled using the Boussinesq approximation. The steady-state Reynolds-averaged Navier-Stokes (RANS) method was used for turbulence modeling to reduce the computational cost [24 ]. The turbulence effect was modeled using Wray-Agarwal (WA) one-equation model [24 , 47 , 48 ]. The turbulent external flows around the buildings of concern were resolved by solving the RANS equation. The Monin-Obukhov similarity theory (MOST) [49 ] was applied to specify the boundary conditions for wind, temperature, and turbulent viscosity [50 ]. All building surfaces and ground were set as non-slip walls. Atmospheric boundary layer profile for neutral flow was used at the inlet of the computational domain. Surface-Energy-balance (SEB) model determines the heat transfer between the wind flow and the building surface. Solar shortwave radiation, thermal longwave radiation, and convective and convection heat transfer are the main mechanisms of the SEB model. The finite volume Discrete Ordinates Method (fvDOM) [51 ] was adopted to simulate the longwave radiation exchange between the urban surfaces and the sky. Using the aerodynamic model in IEM, the wind speed and air temperature values in the climate space can be generated and passed to noise simulation as input parameters.
The noise propagation model in IEM was developed based on the Calculation of Road Traffic Noise (CRTN) [52 ] coupled with the atmospheric refraction model [53 ] for accessing meteorological effects. Once the turbulent wind flow has been simulated using the aerodynamic model in IEM, the calculated wind speed, lapse rate, and wind shear will be passed to the atmospheric refraction model. The atmospheric refraction effects on noise propagation are calculated and considered in the CRTN model. The noise level of the area of concern due to distance attenuation, ground absorption, screening, and site layout effects can be evaluated using the CRTN model. This approach [25 ] allows the adoption of a set of unstructured surface mesh to represent arbitrary 3D building geometry instead of just an extrusion of a building footprint.