“…Some earlier studies have examined the effect of increased ammonia concentration on the microbial dynamics with the use of complex substrates via DNA-based analysis. 10,22 Although microbial community composition analysis by DNA screening provides good insights on the composition of the tested microbial communities, RNA analysis can provide accurate snapshots of the active microbiome in dynamic systems such as AD under ammonia stress.…”
“…Some earlier studies have examined the effect of increased ammonia concentration on the microbial dynamics with the use of complex substrates via DNA-based analysis. 10,22 Although microbial community composition analysis by DNA screening provides good insights on the composition of the tested microbial communities, RNA analysis can provide accurate snapshots of the active microbiome in dynamic systems such as AD under ammonia stress.…”
“…This finding suggested that factors in the environment may have caused the spatial divergence of the microbial communities. Recent studies on the major environmental predictors of microbial diversity indicated that no single factor could explain the biogeographic patterns of microorganisms (Guo et al 2018 ; Meng et al 2020 ; Zeb et al 2019 ). Fig.…”
Recent studies on the microbial community composition of human excrement after rural household toilet treatment are unclear regarding the effects and risks of using recycled products as fertilizers in agriculture. In this study, we used Illumina high-throughput sequencing to investigate the microbial community structure of the excrement from 50 Chinese rural household toilets on a spatial scale, and we evaluated the impact of select geochemical factors on the bacterial and fungal communities in the human excrement. Multivariate analysis showed that there was a significant spatial differentiation of the human excrement in microbial communities after all toilet treatments. Twenty dry toilet samples and thirty septic tank samples had similar bacterial (Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes) and fungal phyla (Ascomycota and Basidiomycota), differing only in the proportions of the microorganisms. For both dry toilet samples and septic tank samples, the pH and ammonium nitrogen were found to be the major driving forces affecting the changes in bacterial community structures (
p
<0.05), while there was no correlation found for the fungal community with environmental factors in China (
p
>0.05), except in the northern regions, where the total phosphorus was found to be significantly correlated with the fungal community (
p
<0.05). Network analysis confirmed that NH
4
+
-N had the most significant impact on the content of pathogens. Certain pathogens were still detected after toilet treatment, such as
Streptococcus
,
Bacteroides
,
Aspergillus
, and
Chrysosporium
, and the proportion of potential pathogenic bacteria in dry toilets was higher than that in septic tanks, suggesting that septic tanks were better than dry toilets in treating human excrement. These results provide an ecological perspective for understanding the large-scale geographic distribution of household excrement microbial communities in rural areas and for improving human excrement treatment technologies and avoiding the risks of agricultural applications.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11356-021-13779-9.
“…The kinetic modeling of the growth of bacterial population, and thus, the biogas production in the anaerobic digester, can be modeled by the Gompertz model [6,25]. First developed to study and describe the mortality rate of humans, the Gompertz model was later modified to reflect the bacterial growth kinetics [26,27].…”
This study evaluates the effects of the varying substrate to inoculum ratios (S:I) of 0.5, 1, 2, 3, 4, 5, and 6 (volatile solids/VS basis) on the kinetics of biogas production during batch mesophilic (35 ± 1 ℃) anaerobic digestion (AD) of simulated food waste (FW), using anaerobic digestate as the inoculum. Kinetic parameters during biogas production (scrubbed with NaOH solution) are predicted by the first-order and the modified Gompertz model. The observed average specific biogas yields are in descending order corresponding to the S:I ratios 1, 2, 4, 6, 3, 5, and 0.5, respectively, and the significant effect of the S:I ratio was observed. The tests with the S:I of 1 have the maximum average biogas production rates of 88.56 NmL/gVS.d, whereas tests with the S:I of 6 exhibited the lowest production rates (24.61 NmL/gVS.d). The maximum biogas yields, predicted by the first order and the modified Gompertz model, are 668.65 NmL/gVS (experimental 674.40 ± 29.10 NmL/gVS) and 653.17 NmL/gVS, respectively. The modified Gompertz model has been proven to be suitable in predicting biogas production from FW. VS removal efficiency is greater in higher S:I ratios, with a maximum of 78.80 % at the S:I ratio of 6, supported by the longer incubation time. Moreover, a significant effect of the S:I ratio is seen on kinetics and energy recovery from the AD of FW.
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