An increase of ammonia emissions from terrestrial ecosystems on the Tibetan Plateau since 1980 deduced from ice core record

Ammonia (NH₃) emissions could have significant impacts on both ecosystems and human health. Ice cores from the Tibetan Plateau contain information about past ammonium (NH₄⁺) deposition, which could yield important insights into historical NH₃ emissions in the surrounding source regions as well as long-distance NH₄⁺ aerosol transport via atmospheric circulation. In this paper, we present a high-resolution atmospheric NH₄⁺ deposition record for the period, 1951-2008, reconstructed from the Zangser Kangri (ZK) ice core in the northern Tibetan Plateau. An empirical orthogonal function (EOF) analysis of major soluble ions (NH₄⁺, NO₃^-, SO₄^2-, Cl^-, Na⁺, K⁺, Mg²⁺ and Ca²⁺) reveals that EOF 1 has significant loadings of all ions, therefore representing common transport pathways, while EOF 2 is only significantly loaded by NH₄⁺ (0.86) and NO₃^- (0.35), suggesting a unique signal possibly representing emissions from the surrounding terrestrial ecosystems on the Tibetan Plateau. Backward trajectory analysis indicates that the air masses over the ZK ice core drilling site primarily come from the northwestern Indian Peninsula. NH₃ emissions from agricultural activities in this area likely contribute to the NH₄⁺ deposition of the ZK ice core via the Indian monsoon. Correlations between EOF 2 time series and temperature, normalized difference vegetation index (NDVI) suggest that increasing temperature and vegetation after 1980 likely promoted NH₃ emissions from terrestrial ecosystems. Our results provide a reliable and valuable assessment of NH₄⁺ deposition from human activities and terrestrial ecosystems in the ZK ice core, and help in understanding air pollution over the past few decades in the northern Tibetan Plateau.

Exploring the Impact of Climate Variability on Malaria Transmission Using a Dynamic Mosquito-Human Malaria Model

Introduction:

The reasons for malaria resurgence mostly in Africa are yet to be well understood. Although the causes are often linked to regional climate change, it is important to understand the impact of climate variability on the dynamics of the disease. However, this is almost impossible without adequate long-term malaria data over the study areas.

Methods:

In this study, we develop a climate-based mosquito-human malaria model to study malaria dynamics in the human population over KwaZulu-Natal, one of the epidemic provinces in South Africa, from 1970-2005. We compare the model output with available observed monthly malaria cases over the province from September 1999 to December 2003. We further use the model outputs to explore the relationship between the climate variables (rainfall and temperature) and malaria incidence over the province using principal component analysis, wavelet power spectrum and wavelet coherence analysis. The model produces a reasonable fit with the observed data and in particular, it captures all the spikes in malaria prevalence.

Results:

Our results highlight the importance of climate factors on malaria transmission and show the seasonality of malaria epidemics over the province. Results from the principal component analyses further suggest that, there are two principal factors associated with climates variables and the model outputs. One of the factors indicate high loadings on Susceptible, Exposed and Infected human, while the other is more correlated with Susceptible and Recovered humans. However, both factors reveal the inverse correlation between Susceptible-Infected and Susceptible-Recovered humans respectively. Through the spectrum analysis, we notice a strong annual cycle of malaria incidence over the province and ascertain a dominant of one year periodicity. Consequently, our findings indicate that an average of 0 to 120-day lag is generally noted over the study period, but the 120-day lag is more associated with temperature than rainfall. This is consistence with other results obtained from our analyses that malaria transmission is more tightly coupled with temperature than with rainfall in KwaZulu-Natal province.