One of the most serious challenges threatening agricultural sustainability in Nigeria is land degradation. Although this issue has received little attention, soil and water conservation practices have been identified as a possible pathway out of the potential problems posed by land degradation. Therefore, the central research question that this paper tries to address is the following: Do adoption of soil and water conservation (SWC) practices affect crop productivity and household welfare? This paper uses data collected by the International Institute of Tropical Agriculture (IITA) from maize farmers in rural Nigeria. We usedemploy the propensity score matching (PSM), inverse probability weighting adjusted regression model (IPWRA) approach, and the linear regression with endogenous treatment effect (LRETE) model to incorporate the typologies of SWC practices, and tested how the model affects crop productivity and household welfare. Additionally, multinomial logit was used to estimate the factors influencing the decision to adopt single and multiple SWC practices. The estimates show that education, age of the household head, access to credit, experience of drought, soil fertility, and occupational stress contribute to the decision to adopt SWC practices. The casual effect estimates reveal that both single and multiple adoptions of SWC practices had a positive and significant relationship with the crop productivity and welfare of the adopters. The results show that the adoption of combined SWC practices has a higher impact on crop productivity and welfare than single SWC practices. For instance, the adoption of a combination of three SWC practices was found to increase crop productivity and household welfare by 27.55% and 38.23%, respectively versus 13.91% and 15.11% in the case of single SWC practices. The study suggests that profile-raising agenda and efforts that focus on promoting the adoption of combination of SWC practices should be designed and implemented to enhance crop productivity and hence the welfare of the maize farming households in rural Nigeria.

PR | IFPRI3; ISI; DCA; 1 Fostering Climate-Resilient and Sustainable Food Supply; 2 Promoting Healthy Diets and Nutrition for all; G Cross-cutting gender theme | DSGD

Because people spend most of their time indoors, the characterization of indoor air quality is important for exposure assessment. Unfortunately, indoor air data are scarce, leading to a major data gap in risk assessment. In this study, PM2.5 concentrations in both indoor and outdoor air were simultaneously measured using on-line particulate counters in 13 households in Haidian, Beijing for both heating and non-heating seasons. A bimodal distribution of PM2.5 concentrations suggests rapid transitions between polluted and non-polluted situations. The PM2.5 concentrations in indoor and outdoor air varied synchronously, with the indoor variation lagging. The lag time in the heating season was longer than that in the non-heating season. The particle sizes in indoor air were smaller than those in ambient air in the heating season and vice versa in the non-heating season. PM2.5 concentrations in indoor air were generally lower than those in ambient air except when ambient concentrations dropped sharply to very low levels or there were internal emissions from cooking or other activities. The effectiveness of an air cleaner to reduce indoor PM2.5 concentrations was demonstrated. Non-linear regression models were developed to predict indoor air PM2.5 concentrations based on ambient data with lag time incorporated. The models were applied to estimate the overall population exposure to PM2.5 and the health consequences in Haidian. The health impacts would be significantly overestimated without the indoor exposure being taken into consideration, and this bias would increase as the ambient air quality improved in the future.

Atmospheric PM10 were measured for 12 months at 18 sites along a 2500 km profile across northern China. Annual mean PM10 concentrations in urban, rural village, and rural field sites were 180 ± 171, 182 ± 154, and 128 ± 89 μg/m3, respectively. The similarities in PM10 concentrations between urban and rural village sites suggest that strong localized emissions and severe contamination in rural residential areas are derived from solid fuels combustion in households. High PM10 concentrations in Wuwei and Taiyuan were caused by either sandstorms or industrial activities. Relatively low PM10 concentrations were observed in coastal areas of Dalian and Yantai. Particulate air pollution was much higher in winter and spring than in summer and fall. Multiple regression analysis indicates that 35% of the total variance can be attributed to sandstorms, precipitation and residential energy consumption. Over 40% of the measurements in both urban and rural village areas exceeded the national ambient air quality standard.

To identify different NO₃ ⁻ sources in surface water and to estimate their proportional contribution to the nitrate mixture in surface water, a dual isotope and a Bayesian isotope mixing model have been applied for six different surface waters affected by agriculture, greenhouses in an agricultural area, and households. Annual mean δ¹⁵N–NO₃ ⁻ were between 8.0 and 19.4‰, while annual mean δ¹⁸O–NO₃ ⁻ were given by 4.5–30.7‰. SIAR was used to estimate the proportional contribution of five potential NO₃ ⁻ sources (NO₃ ⁻ in precipitation, NO₃ ⁻ fertilizer, NH₄ ⁺ in fertilizer and rain, soil N, and manure and sewage). SIAR showed that “manure and sewage” contributed highest, “soil N”, “NO₃ ⁻ fertilizer” and “NH₄ ⁺ in fertilizer and rain” contributed middle, and “NO₃ ⁻ in precipitation” contributed least. The SIAR output can be considered as a “fingerprint” for the NO₃ ⁻ source contributions. However, the wide range of isotope values observed in surface water and of the NO₃ ⁻ sources limit its applicability.

Biomass burning as fuel in the traditional grass-roofed rural households of Western Province of Kenya in open fire places, in poorly ventilated conditions, lead to accumulation of soot under the roofs. This study characterized and quantified the polycyclic aromatic hydrocarbons (PAHs) in accumulated soot in these households and determined the variation in PAHs concentrations with fuel biomass type. Soot samples collected from the households were extracted, cleaned and analysed by gas chromatography. The PAHs were identified using retention times, verified by gas chromatographic mass spectral analysis and quantified from peak area responses using the internal standard method. The PAHs levels significantly varied (P≤0.05) with biomass type in the order: dung≥indigenous trees≥exotic trees≥shrubs and crop residues. Use of dung and wood from indigenous trees as fuel should be discouraged since they are higher emitters (P≤0.05) of carcinogenic PAHs.

Anthropogenic perfluorinated compounds (PFCs), especially the perfluoroalkyl acids (PFAAs) are ubiquitously found in surface waters around the globe. Emissions from households, industries and also atmospheric transport/deposition are discussed as the possible sources. In this study, these sources are evaluated using Switzerland as the study area. Forty-four surface water locations in different rivers and an Alpine lake were investigated for 14 PFAAs, four precursors and acesulfame, an artificial sweetener used as a population marker. Concentrations of individual PFAAs were generally low, between 0.02 and 10 ng/L. Correlation analysis showed that some PFAAs concentrations correlated well with population and less with catchment area, indicating that emissions from population, i.e., from consumer products, is the most important source to surface waters in Switzerland. The correlation with the population marker acesulfame confirmed this observation but highlighted also a few elevated PFAA levels, some of which could be attributed to industrial emissions.

Indoor air contributes significantly to overall exposure, particularly for rural Chinese who often use solid fuels for cooking and/or heating. Unfortunately, overlooked rural indoor air leads to a critical knowledge gap. Simultaneous measurements in the kitchen, living room, and immediately outside of houses using six-channel particle counters were carried out in 18 biomass-burning rural and 3 non-biomass-burning urban households (as a comparison) in winter to characterize dynamic change patterns indoor air pollution and indoor-outdoor relationship. The rural households mainly used wood or crop residues for cooking and heating, while the urban households used pipelined natural gas for cooking and air conditioners for heating. In rural households with significant solid-fuel burning internal sources, the highest concentration was found in the kitchen (101 ± 56 μg/m³), with comparable levels in the living room (99 ± 46 μg/m³) and low levels in outdoor air (91 ± 39 μg/m³). A generally opposite direction of indoor-outdoor exchange was found between the rural and urban households. PM in kitchen air is smaller than that in living rooms and outdoors because solid fuel burning (mainly in rural households) and cooking oil heating (in rural and urban households). Indoor and outdoor PM concentration changed synchronously, with a slight delay in indoor air in urban households but a slight delay in outdoor air in rural households. Cooking, heating, and smoking elevated indoor PM significantly, but different from the cooking activity that produced peaks lasting for about 30 min, emissions from heating created a series of peaks due to frequent disturbance and fuel-feeding and had more significant impacts on the daily average concentration. Distinct indoor-outdoor relationships and dynamic change patterns between the two household categories w/o strong internal biomass burning sources imply that totally different model schemes are needed to quantitatively address indoor air pollution and inhalation exposure.

A previous study observed high blood lead levels (BLL) in preschool children attending 50 day care centers (DCC) in São Paulo, Brazil.To identify whether lead levels found in both homes and DCC environments are associated with high blood lead levels.Children attending 4 DCCs, quoted here as NR, VA, PS and PF, were divided into two groups according to BLL: high exposure (HE: ≥13.9 μg/dL; 97.5 percentile of the 2013 year sample) and low exposure (LE: <5 μg/dL). For in situ lead measurements (lead paint mode: mg/cm² and ROHS mode: μg/g) in the children's households and in the DCC environments, a field portable X-ray-fluorescence analyzer was used. Multiple logistic regressions were performed to control for confounding factors. Odds ratios were adjusted for age, sex, day care center's measured lead, and tobacco.In an NR DCC building, 33.8% of the measurements had lead levels >600 μg/g, whereas such levels were observed in 77.1% of NR playground measurements. In VA DCC, 22% and 23% of the measurements in the building and in the playgrounds had levels higher than 600 μg/g, respectively. The percentage of high lead levels in the children's houses of the LE group was 5.9% (95% CI: 4.3–7.6%) and 13.2 (95% CI: 8.3–18.0%) in the HE group. Moreover, a significant association was found between high BLLs and lead levels found both in households and DCCs (p < 0.001). Most of the high lead measurements were found in tiles and playground equipment.Lead exposure estimated from the DCCs, where children spend about 10 h/day, can be as relevant as their household exposure. Therefore, public authorities should render efforts to provide a rigorous surveillance for lead-free painting supplies and for all objects offered to children.