MOESM1 of A novel close-circulating vapor stripping-vapor permeation technique for boosting biobutanol production and recovery

Additional file 1: Fig. S1. Change in the swelling degree (%) of the PDMS membrane under different butanol titers in feed. Fig. S2. Effect of feed butanol titer on the VSVP performance using PDMS membrane

Organic Carbon Storage in China's Urban Areas

<div><p>China has been experiencing rapid urbanization in parallel with its economic boom over the past three decades. To date, the organic carbon storage in China's urban areas has not been quantified. Here, using data compiled from literature review and statistical yearbooks, we estimated that total carbon storage in China's urban areas was 577±60 Tg C (1 Tg  = 10¹² g) in 2006. Soil was the largest contributor to total carbon storage (56%), followed by buildings (36%), and vegetation (7%), while carbon storage in humans was relatively small (1%). The carbon density in China's urban areas was 17.1±1.8 kg C m⁻², about two times the national average of all lands. The most sensitive variable in estimating urban carbon storage was urban area. Examining urban carbon storages over a wide range of spatial extents in China and in the United States, we found a strong linear relationship between total urban carbon storage and total urban area, with a specific urban carbon storage of 16 Tg C for every 1,000 km² urban area. This value might be useful for estimating urban carbon storage at regional to global scales. Our results also showed that the fraction of carbon storage in urban green spaces was still much lower in China relative to western countries, suggesting a great potential to mitigate climate change through urban greening and green spaces management in China.</p></div

Refinements on the exact method to solve the numerical difficulties in fitting the log binomial regression model for estimating relative risk

Fitting a log binomial regression model using standard software can result in numerical difficulties because its functional form does not restrict the estimated probabilities to values not exceeding unity. The common approaches to resolve the issue are to introduce a constraint to limit the results of each iteration. However, if the ML solution lies on the boundary of the allowable parameter space, some fitted probabilities are equal to unity (named as boundary vector). The common approaches have trouble dealing with those boundary vectors and can only reach somewhere before the ML solution. Previously a remedy has been proposed, but without the details necessary to implement it. Here we provide these details, including formulae for estimating the covariances essential to implement the method, an explanation of inter-dependency between coefficient estimates, and a proof that the method can be implemented in general. Code written for R implements choice of fitting algorithms, finding appropriate starting values, identifying the covariate vector(s) with a fitted probability of unity, and strategies for covariate ordering to handle the issues caused by the common values in two distinct boundary vectors. The model-fitting results are compared with two alternative methods by simulation and example data.</p

Carbon storage in four major pools in China's urban areas in 2006 at 95% confidence intervals (i.e., the mean±1.96× standard error).

<p>Carbon storage in four major pools in China's urban areas in 2006 at 95% confidence intervals (i.e., the mean±1.96× standard error).</p

Relationship between urban carbon storage and urban area (a) or urban carbon density (b) at the provincial level.

<p>The confidence intervals are the same as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0071975#pone-0071975-g001" target="_blank">Figure 1</a>.</p

Comparisons of the total urban carbon storage and urban areas (a), and the fractions of four major carbon pools (b) between China and the Conterminous United States.

<p>Comparisons of the total urban carbon storage and urban areas (a), and the fractions of four major carbon pools (b) between China and the Conterminous United States.</p

The proportion of total carbon in urban areas of China stored in four major carbon pools at regional scales and for the country as a whole for the year 2006.

<p>The proportion of total carbon in urban areas of China stored in four major carbon pools at regional scales and for the country as a whole for the year 2006.</p

Sensitivity of total urban carbon storage to a 10% increase in the each of the input variables.

<p>Sensitivity of total urban carbon storage to a 10% increase in the each of the input variables.</p

Carbon storage and density in urban areas of China in 2006 at national and regional scales at 95% confidence intervals.

<p>Carbon storage and density in urban areas of China in 2006 at national and regional scales at 95% confidence intervals.</p

Carbon storage (Tg) and density (kg C m⁻²) in the provinces (municipalities or autonomous regions) in China's urban areas in 2006 (except Taiwan, Hong Kong and Macao).

<p>Carbon storage is the sum of four pools: vegetation, soils, humans and buildings. The confidence intervals are the same as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0071975#pone-0071975-g001" target="_blank">Figure 1</a>.</p